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Libraries of the future 

J.C.R Licklider 




BRODART, CO. Cat. No. 23-221 

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J. Cy R. Licklider 


Massachusetts Institute of Technology Tl | 
Cambridge, Massachusetts 

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THIS REPORT of research on concepts and problems of 
“Libraries of the Future” records the result of a two-year 
inquiry into the applicability of some of the newer tech- 
niques for handling information to what goes at present 
by the name of library work —i.e., the operations con- 
nected with assembling information in recorded form and 
of organizing and making it available for use. 

Mankind has been complaining about the quantity of 
reading matter and the scarcity of time for reading it at 
least since the days of Leviticus, and in our own day 
these complaints have become increasingly numerous and 
shrill. But as Vannevar Bush pointed out in the article 
that may be said to have opened the current campaign 
on the “information problem,” 

The difficulty seems to be, not so much that we publish un- 



duly in view of the extent and variety of present-day interests, 
but rather that publication has been extended far beyond our 
present ability to make real use of the record. The summa- 
tion of human experience is being expanded at a prodigious 
rate, and the means we use for threading through the con- 
sequent maze to the momentarily important item is the same 
as was used in the days of square-rigged ships.* 

It has for some time been increasingly apparent that 
research libraries are becoming choked from the prolif- 
eration of publication, and that the resulting problems 
are not of a kind that respond to merely more of the same 
—ever and ever larger bookstacks and ever and ever 
more complicated catalogues. It was with this realization 
that the Ford Foundation in 1956 established the Coun- 
cil on Library Resources to assist in attempts to discover 
solutions to these problems and to bring the benefits of 
modern technology to the correction of maladjustments 
for which modern technology is to a large degree re- 
sponsible. Somewhat later the Foundation earmarked a 
specific sum to enable the Council to concentrate its work 
in the storage and retrieval of information in a center 
involving the activities of specialized personnel. 

Accordingly, early in 1961 the Council commenced a 
search for an appropriate site and for qualified investiga- 
tors to undertake an inquiry into the characteristics of 
the “library of the future.” In this search it consulted a 
number of persons especially thoughtful and knowledge- 
able in this nebulous area. Among them were Dr. Wil- 
liam O. Baker, Vice-President for Research, Bell Tele- 
phone Laboratories; Dr. Lloyd V. Berkner, President, 
Graduate Center of the Southwest; Dr. Richard H. Bolt, 
Chairman of the Board, Bolt Beranek and Newman Inc., 

* Vannevar Bush, As We May Think. Atlantic Monthly, 176, 
101-108, July 1945. 



and at that time also Associate Director for Research, 
National Science Foundation; Dr. Caryl P. Haskins, 
President, Carnegie Institution of Washington; Dr. Gil- 
bert W. King, at that time Director for Research, Inter- 
national Business Machines Corporation, now Director 
of Research, Itek Corporation; Dr. Edwin H. Land, Pres- 
ident, Polaroid Company; Prof. Philip M. Morse, Pro- 
fessor of Physics and Director of the Computation Lab- 
oratory, Massachusetts Institute of Technology; Dr. John 
R. Pierce, Director of Research in Communications Fun- 
damentals, Bell Telephone Laboratories: Dr. Emanuel 
R. Piore, Vice-President for Research and Engineering, 
International Business Machines Corporation; Dr. Earl 
P. Stevenson, then Chairman, since Consultant, Arthur 
D. Little, Inc.; and Dr. Warren Weaver, Vice-President, 
Alfred P. Sloan Foundation. 

There is perhaps no question that makes more instant 
demand upon the combined experience and imagination 
of the respondents, or as a result more widely differen- 
tiates one response from another, than does the question, 
“How should one explore the library of the future?” In 
this matter, too, the pattern was set by Dr. Bush in his 
1945 article, to which reference has already been made, 
in which he invented the “Memex,” the private memory 
device in which all a man’s records may be stored, linked 
by associative indexing and instantly ready for his use. 
Just so, in its consultations the Council received as many 
answers as the number of persons whom it questioned, 
each answer widely different from the last: from one, an 
exhortation to investigate the fundamental processes of 
cognition; from another, an admonition on the impor- 
tance of building consecutively from things as they are 
to things as they may be; from a third, a case history 



demonstrating the essential role of serendipity in the so- 
lution of difficult problems. 

In one particular and only one was there agreement 
among the consultants: find the right man. And more 
and more frequently, as the consultations proceeded, the 
name of an individual emerged. 

Dr. J. C. R. Licklider was at that time the supervisory 
engineering psychologist of Bolt Beranek and Newman 
Inc. of Cambridge, Massachusetts, consulting engineers 
with a primary interest in acoustics. (Dr. Licklider had 
been President of the Acoustical Society of America in 
1958.) Behind him, at Harvard and the Massachusetts 
Institute of Technology, Dr. Licklider had left an en- 
viable record of research on problems of human com- 
munication and the processing and presentation of infor- 
mation. This combination of training and experience 
seemed to the Council to offer an admirable background 
from which to prospect the “library of the future.” On 
his side, Dr. Licklider was attracted by the problem and 
almost overnight wrote an eloquent prospectus for the 
first year’s work. This, with very slight revision, was 
adopted, and the study commenced in November 1961. 

In October 1962, Dr. Licklider took a year’s leave of 
absence from Bolt Beranek and Newman on a special 
assignment for the Department of Defense. However, the 
“research on concepts and problems of libraries of the 
future” continued under his general direction in his ab- 
sence. But when the year came around again it was not 
found possible to extend the relationship, and the study 
was brought to an end with the rendition, in January 
1964, of the final report upon which the present volume 
is based. 

The reader will not find here that a bridge has been 



completed from things as they are to things as they may 
be, but he will find a structure on which he can take some 
steps out from the here and now and dimly descry the 
may be on the other side. 

Council on Library Resources, Inc. 
Washington, D.C. 

August 1, 1964 


THE sTUDY on which this report is based was sponsored 
by the Council on Library Resources, Inc., and con- 
ducted by Bolt Beranek and Newman Inc., between 
November 1961 and November 1963. I acknowledge 
with deep appreciation the contributions of inspiration, 
thought, fact, and effort made by members of the two 

The Council on Library Resources defined the gen- 
eral scope of the work and maintained, through its 
officers and staff and a special Advisory Committee, a 
spirited interaction with the contractor’s group. I offer 
special thanks to Verner W. Clapp, President of the 
Council, Melville J. Ruggles, Vice-President, and Lau- 
rence B. Heilprin, Staff Scientist, for frequent infusions 
of wisdom and knowledge. The Chairman of the Ad- 



visory Committee, Joseph C. Morris, was a vigorous acti- 
vator and a source of much encouragement. To him and to 
the members of the Committee — Gilbert W. Chapman, 
Caryl P. Haskins, Barnaby C. Keeney, Gilbert W. King, 
Philip M. Morse, and John W. Pierce — and to Lyman 
H. Butterfield, who was closely associated with the Com- 
mittee, I express appreciation for a rare blend of adminis- 
trative guidance and constructive technical criticism. 

The colleagues within Bolt Beranek and Newman who 
participated most actively in the library study were 
Fisher S. Black, Richard H. Bolt, Lewis C. Clapp, 
Jerome I. Elkind, Mario Grignetti, Thomas M. Marill, 
John W. Senders, and John A. Swets (who directed the 
research during the second year of the study). 

John McCarthy, Marvin Minsky, Bert Bloom, Daniel 
G. Bobrow, Richard Y. Kain, David Park, and Bert 
Raphael of the Massachusetts Institute of Technology 
were also part of the research group. The opportunity to 
work with those BBN and M.I.T. people was exciting 
and rewarding. I am appreciative of their comradeship 
and their contributions. I hope that I have done fair 
justice to their ideas and conclusions in Part H, which 
summarizes the individual researches that comprise the 

Perhaps the main external influence that shaped the 
ideas of this book had its effect indirectly, through the 
community, for it was not until Carl Overhage noticed 
its omission from the References that I read Vannevar 
Bush’s “As We May Think” (Atlantic Monthly, 176, 
101-108, July 1945). I had often heard about Memex 
and its “trails of references.” I had hoped to demon- 
strate Symbiont to Dr. Bush as a small step in the direc- 
tion in which he had pointed in his pioneer article. But 



I had not read the article. Now that I have read it, I 
should like to dedicate this book, however unworthy it 
may be, to Dr. Bush. 


Mt. Kisco, New York 
November 4, 1964 



The Role of Schemata 
Pages, Books, and Libraries 
The Relevance of Digital Computers 

PART |! 


1. The Size of the Body of Recorded Information 

Estimates of the Size 

Size of the Corpus versus Capacity of Computer 
Memories and Speed of Computer Processors 




2. Aims, Requirements, Plans, and Criteria for 
Procognitive Systems 
Acquisition of Knowledge 
Organization of Knowledge 
Use of Knowledge 

Processing versus Control and Monitoring of 

Criteria for Procognitive Systems 

Plan for a System to Mediate Interactions with the 
Fund of Knowledge 

Steps toward Realization of Procognitive Systems 

3. Information Storage, Organization, and Retrieval 

Systems Based on Sets and Subsets 
Space Analogues 

Functions and Relations 

Predicate Calculus 

Higher-Order Languages 

4. Man-Computer Interaction in Procognitive Systems 

The Physical “Intermedium” 
Man-Computer Interaction Languages 

Adaptive Self-Organization in Man-Computer 



5. Syntactic Analysis of Natural Language by 

6. Research on Quantitative Aspects of Files and Text 

On the Length of a Class of Serial Files 
Entropy of Words in Printed English 







7. A Measure of the Effectiveness of Information- 

Retrieval Systems 148 
8. Libraries and Question-Answering Systems 152 
9. Studies of Computer Techniques and Procedures 157 
An “Executive” Program to Facilitate the Use of the 
PDP-1I Computer 158 
On-Line Man-Computer Communication 170 
A File Inverter 174 
An Automated Card Catalogue 1/3 
A System to Facilitate the Study of Documents 177 
Associative Chaining as an Information-Retrieval 
Technique 179 
Two Question-Answering Systems 182 
An Approach to Computer Processing of 
Natural Language 192 
References 205 
Index 209 



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For Two YEARS beginning in November 1961, a small 
group of engineers and psychologists at Bolt Beranek 
and Newman Inc. explored “concepts and problems of 
libraries of the future” under the sponsorship of the Coun- 
cil on Library Resources. This is a summary report of the 
study. It has two main parts. If the phrases were not so 
long, the parts would be entitled: (1) Concepts and Prob- 
lems of Man’s Interaction with the Body of Recorded 
Knowledge, and (2) Explorations in the Use of Com- 
puters in Information Storage, Organization, and Re- 


The “libraries” of the phrase, “libraries of the future,” 
may not be very much like present-day libraries, and the 



term “library,” rooted in “book,” is not truly appropriate 
to the kind of system on which the study focused. We 
delimited the scope of the study, almost at the outset, to 
functions, classes of information, and domains of knowl- 
edge in which the items of basic interest are not the print 
or paper, and not the words and sentences themselves — 
but the facts, concepts, principles, and ideas that lie 
behind the visible and tangible aspects of documents. 
The criterion question for the delimitation was: “Can it 
be rephrased without significant loss?” Thus we delimited 
the scope to include only “transformable information.” 
Works of art are clearly beyond that scope, for they 
suffer even from reproduction. Works of literature are 
beyond it also, though not as far. Within the scope lie 
secondary parts of art and literature, most of history, 
medicine, and law, and almost all of science, technology, 
and the records of business and government. 


The “future,” in “libraries of the future,” was defined 
at the outset, in response to a suggestion from the Coun- 
cil, as the year 2000. It is difficult, of course, to think 
about man’s interaction with recorded knowledge at so 
distant a time. Very great and pertinent advances doubt- 
less can be made during the remainder of this century, 
both in information technology and in the ways man uses 
it. Whether very great and pertinent advances will be 
made, however, depends strongly on how societies and 
nations set their goals. Moreover, the “system” of man’s 
development and use of knowledge is regenerative. If a 
strong effort is made to improve that system, then the 
early results will facilitate subsequent phases of the effort, 



and so on, progressively, in an exponential crescendo. 
On the other hand, if intellectual processes and their 
technological bases are neglected, then goals that could 
have been achieved will remain remote, and proponents 
of their achievement will find it difficult to disprove 
charges of irresponsibility and autism. 

The remoteness of the planning target date, neverthe- 
less, had a desirable influence on our thinking. It made 
it impossible to accept tacitly the constraints that tend 
to be imposed upon imagination by the recent course and 
current trend of technology. It freed us to concentrate 
upon what man would like the nature of his interaction 
with knowledge to be. That is possibly an important 
freedom, for extrapolation of the main courses of present- 
day library science and information technology does not 
lead to concepts or systems that seem either very desira- 
ble or very effective. 


Freedom from constraints imposed by existing con- 
cepts and devices, however, is double-edged. According 
to the most advanced theories of cognition, men think by 
manipulating, modifying, and combining “schemata.” A 
new concept is achieved, not by creating a new schema 
ab initio, on a custom basis, but by adapting an old 
schema or, if necessary, arranging several refurbished 
schemata into a new, complex structure. If we renounce 
schemata derived from experience with existing library 
systems, file rooms, and computer centers, therefore, we 
have to be careful not to leave ourselves without parts 
from which to construct new concepts. A guideline for 
avoiding that predicament is to discard the upper-echelon 



schemata — those at the level of system and subsystem — 
and to retain, for possible alteration and reuse, the lower- 
echelon, component-level schemata. 

It is not possible, in a summary report, to present a 
complete inventory of promising component-level sche- 
mata, but it may be helpful to illustrate the idea of dis- 
carding schemata at the system and subsystem levels while 
retaining those at the component level. The illustration 
will take the form of comments about pages (compo- 
nents), books (subsystems), and libraries (systems). 


As a medium for the display of information, the printed 
page is superb. It affords enough resolution to meet the 
eye’s demand. It presents enough information to occupy 
the reader for a convenient quantum of time. It offers 
great flexibility of font and format. It lets the reader con- 
trol the mode and rate of inspection. It is small, light, 
movable, cuttable, clippable, pastable, replicable, dis- 
posable, and inexpensive. Those positive attributes all 
relate, as indicated, to the display function. The tallies 
that could be made for the storage, organization, and re- 
trieval functions are less favorable. 

When printed pages are bound together to make books 
or journals, many of the display features of the individual 
pages are diminished or destroyed. Books are bulky and 
heavy. They contain much more information than the 
reader can apprehend at any given moment, and the ex- 
cess often hides the part he wants to see. Books are too 
expensive for universal private ownership, and they cir- 
culate too slowly to permit the development of an effi- 
cient public utility. Thus, except for use in consecutive 



reading — which is not the modal application in the do- 
main of our study — books are not very good display de- 
vices. In fulfilling the storage function, they are only fair. 
With respect to retrievability they are poor. And when it 
comes to organizing the body of knowledge, or even to 
indexing and abstracting it, books by themselves make 
no active contribution at all. 

If books are intrinsically less than satisfactory for the 
storage, organization, retrieval, and display of informa- 
tion, then libraries of books are bound to be less than 
satisfactory also. We may seek out inefficiencies in the 
organization of libraries, but the fundamental problem 
is not to be solved solely by improving library organiza- 
tion at the system level. Indeed, if human interaction 
with the body of knowledge is conceived of as a dynamic 
process involving repeated examinations and intercom- 
parisons of very many small and scattered parts, then any 
concept of a library that begins with books on shelves is 
sure to encounter trouble. Surveying a million books on 
ten thousand shelves, one might suppose that the diffi- 
culty is basically logistic, that it derives from the gross 
physical arrangement. In part, of course, that is true, 
but in much greater part the trouble stems from what 
we may call the “passiveness” of the printed page. When 
information is stored in books, there is no practical way 
to transfer the information from the store to the user 
without physically moving the book or the reader or both. 
Moreover, there is no way to determine prescribed func- 
tions of descriptively specified informational arguments 
within the books without asking the reader to carry out 
all the necessary operations himself. 

We are so inured to the passiveness of pages and books 
that we tend to shrug and ask, “Do you suggest that the 



document read its own print?” Surely, however, the diffi- 
culty of separating the information in books from the 
pages, and the absence, in books, of active processors, 
are the roots of the most serious shortcomings of our 
present system for interacting with the body of recorded 
knowledge. We need to substitute for the book a device 
that will make it easy to transmit information without 
transporting material, and that will not only present in- 
formation to people but also process it for them, follow- 
ing procedures they specify, apply, monitor, and, if 
necessary, revise and reapply. To provide those services, 
a meld of library and computer is evidently required. 
Let us return now to the problem of schemata from 
which to construct future systems to facilitate man’s inter- 
action with transformable information. As a shorter term 
for such systems, let us use “procognitive systems.” * In 
thinking about procognitive systems, we should be pre- 
pared to reject the schema of the physical library — the 
arrangement of shelves, card indexes, check-out desks, 
reading rooms, and so forth. That schema is essentially 
a response to books and to their proliferation. If it were 
not for books, and for the physical characteristics of 

* “Procognitive systems” is also more appropriate than “library 
systems of the future” to designate the objects of our study. “Systems” 
has, for us, the proper connotations. “Future” is correct, but it should 
not be necessary to repeat it explicitly throughout the discussion. The 
systems in which we are interested are broader than present-day li- 
braries; the systems will extend farther into the process of generating, 
organizing, and using knowledge. Moreover, since the idea of “book” 
is not likely to be central, it seems best to substitute another word 
for “library.” Since the systems are intended to promote the advance- 
ment and application of knowledge, they are “for knowledge,” and 
thus procognitive systems. When this term is used in the plural, it 
refers to specialized systems as well as to the general, neolibrary 
system, and sometimes to successive generations of such systems. When 
it is used in the singular, it refers to the neolibrary system of the as- 
sumed epoch. 



books that we have discussed, there would be no raison 
d’étre for many parts of the schema of the physical li- 

At the level of subsystem, we should be prepared to 
reject the schema of the physical book itself, the passive 
repository for printed information. That involves reject- 
ing the printed page as a long-term storage device, 
though not for short-term storage and display. 

At the component level, on the other hand, there are 
few library and documentation schemata that we should 
wholly reject, and many that we should retain. In addi- 
tion to the schema of the printed page, we should retain 
schemata corresponding, for example, to: 

1. Hierarchies of segments of text, such as the hier- 

archy of character, word, . . . sentence, paragraph, 
sclapter, .. . volume. ... 

2. The concepts of textual, tabular, graphical, and pic- 
torial information. 

3. Such concepts as title, author, abstract, body, foot- 
note, and list of references. 

4. Such concepts as original article, review article, 
note, letter, journal, and book.* 

5. Such concepts as catalogue, index, descriptor, Uni- 
term, and thesaurus. 

Although the foregoing constitutes a much abbreviated 
and perhaps only suggestive discussion of the relation of 
existing libraries to future procognitive systems, it may 
serve as an introductory clarification of the notion of 
selective retention of schemata for use in planning. The 
same notion is applicable to documentation centers, spe- 

* In the sense of classes of information, not physical carriers of in- 


cialized information storage and retrieval systems, and 
digital computing centers. A few remarks about digital 
computing centers will bring this topic to a close. 


The over-all plan of organization of the typical uni- 
versity or business computing center does not provide a 
good system schema for our purposes. If one thinks of 
“computing” in terms of collecting data and writing a 
computer program, having the data and program punched 
into cards, delivering the cards to a computer center in 
the morning, and picking up a pile of “printouts” in the 
afternoon, and so forth, he is likely to scoff at the idea 
that the science and technology of computing provide a 
large fraction of the extant ideas that are relevant to, 
and promising for, future procognitive systems. On the 
other hand, if one looks at the echelon below that of the 
computing center, he finds many promising schemata 
among the concepts, techniques, and devices. The most 
valuable are, by and large, the most abstract, and even 
those that are highly abstract may require much modifi- 
cation to fit into a system schema of the kind that we 
require. Almost surely, however, some of the informa- 
tion-processing schemata suggested by the following will 
play a role in shaping future procognitive systems: 

Random-access memory, 

Content-addressable memory, 

Parallel processing, 

Cathode-ray-oscilloscope displays and light pens, 
Procedures, subroutines, and related components 
of computer programs, 




. Hierarchical and recursive program structures, 
7. List structures, 
8. Procedure-oriented and problem-oriented lan- 
9. Xerographic output units, 
10. Time-sharing computer systems with remote user 

What is of value for our purpose is not, for example, 
the oscilloscope or the light pen. It is the schema in 
which a man sits at a desk, writes or draws on a surface 
with a stylus, and thereby communicates to a programmed 
information processor with a large memory. It is the 
mental image of the immediate response, visible on the 
oscilloscope, through which the computer acknowledges 
the command and reports the consequences of carrying 
it out — in which the computer acknowledges the ques- 
tion and presents an answer. Without such schemata in 
mind, one cannot think effectively about future sys- 
tems for interaction with the body of knowledge. With 
such schemata, and enough others suggested by experi- 
ences in other contributory fields, perhaps conceptual 
progress can be made. 

It is important to recognize that our progress must, 
for a time, be largely conceptual or demonstrational. 
Present-day information-processing machinery cannot 
process usefully the trillions of bits of information in 
which the body of knowledge is clothed (or hidden), nor 
can it handle significant subsets efficiently enough to 
make computer processing of the textual corpus of a field 
of engineering, for example, useful as a tool in everyday 
engineering and development. The things of interest that 
the present computers can do usefully are (1) process 



data in experimental studies, and (2) simulate and 
demonstrate techniques and systems which, although they 
cannot yet be implemented fully, can be set forth in a 
dynamic form that is sufficiently realistic to facilitate 
evaluation and further investigation. The latter seems 
to us to be a particularly promising pursuit. 




Our examination of concepts and problems in the do- 
main of procognitive systems dealt with four topics: 

1. Information measures of the world’s store of knowl- 

2. Aims, requirements, criteria, and plans for pro- 
cognitive systems. 

3. Schemata for storage, organization, retrieval, and 
dissemination of information. 

4. Man-computer interaction in procognitive systems. 

The main lines of study, and the projections and conclu- 
sions to which they led, are set forth in the following 

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The Size of the Body 
of Recorded Information 


As a basis for thinking about procognitive systems, one 
needs an estimate of how much information there is to 
cope with. The concepts — information measure and in- 
formational redundancy — are subtle; the simplest esti- 
mate needed is not. The simplest estimate needed is the 
number of alphanumeric characters that would be re- 
quired to spell out the contents of all the documents in 
the libraries of the world, each document “type” (as op- 
posed to document “token,” or individual copy) being 
considered only once. An adjustment would have to be 
made to take into account pictures and other nonalpha- 
numeric contents. Answers would be determined for such 
questions as, “Does translation from one language to an- 



other create a new document type?” Various subdivisions 
of the total into parts are of interest. Even with those 
qualifications, however, the question of the total number 
of characters in the corpus is fairly simple and direct. 

If a definite number of “bits” is assigned to each al- 
phanumeric character, it is possible to multiply the total 
number of characters by the number of bits per character 
and say something like: “There are n bits of recorded 
information in the world’s libraries.” Or “It would take 
n cells of binary storage space to hold one copy of each 
document in all the world’s libraries.” The second state- 
ment seems preferable to the first. It is not clear, however, 
that converting from characters to bits offers any advan- 
tage other than the adventitious one of reconciling two 
estimates made in the course of our study. 

During the first few months, a very rough estimate was 
made (Licklider, 1962), based mainly on the work of 
Bourne (1961) and on the size of the Library of Con- 
gress, together with some miscellaneous impressions. The 
first estimate gave 2 - 10'* characters or (at 5 bits per 
character) 10% bits.* Later, Senders (1963), after a 
much more careful study, estimated that the total lies be- 
tween 3.8 - 1013 and 3.8 - 10'* characters or (at 12 bits 
per character) between 4.6 - 10%* and 4.6 - 10* bits. 
The difference between the assumptions about exploita- 
tion of redundancy in the coding of characters (5 or 6 
versus 12 bits per character), together with the round- 
off, almost exactly compensates for the difference be- 
tween the estimates of the number of characters. 

For our purposes, there is no need to resolve such 

* Six bits per character was the initial assumption. In 6-2 - 10% = 
1.2 - 10°, however, there is an unwarranted appearance of precision. 
We therefore used 5 bits per character as a temporary expedient. 



“small” discrepancies. Let us merely average Senders’ 
bounds and conclude that there are roughly 10** charac- 
ters and 10*° bits in the total store. The size of the store 
is doubling every 15 or 20 years, which makes the cur- 
rent growth rate about 2 - 10° bits per second (Senders, 
1963). We might make the working assumption that 
there will be 2 - 10” bits in 1980 and 5 - 10” bits in the 
year 2000. 

If we accept 10” bits as the present total, then we may 
take about 10** as the number of bits required to hold all 
of science and technology, and 10" for “solid” * science 
and technology. Then, if we divide science and technol- 
ogy into 100 “fields” and 1000 “subfields,” we come out 
with 10” bits for a field, on the average, and 10’° bits 
or a billion characters for a subfield. 

To relate the foregoing estimates to common experi- 
ence, we may start with a printed page. If we assume 
pages with 100 characters per line and 50 lines, we have 
5000 characters per page. Then, assuming 200 pages per 
book, we have 10° characters per book. Thus the “solid” 
literature of a subfield is the equivalent of a thousand 
books, and the total literature of a subfield is the equiva- 
lent of ten thousand books. If one thinks of information 
theory or psychophysics as a subfield, the figures seem 
not to violate intuition. 


One of the main prerequisites for effective organiza- 
tion of the body of knowledge is — if we may anticipate 

* “Solid” is intended to delimit the literature by excluding popular- 
izations, ephemeral items, and contributions from unqualified sources. 



a conclusion to be developed later — to get the corpus, 
either all at once or a large cluster at a time, into a process- 
ible memory. How, then, do the estimates set forth in 
the foregoing section compare with estimates of the 
computer’s memory size, both present and future? And 
how do estimates of the computer’s processing capability 
compare with estimates of the amount of processing that 
would have to be done to organize the body of knowledge 
broadly and deeply? 

Access to information requires time. Usually, two or 
more different “access times” must be considered. Even if 
one knows precisely the location of a passage that he 
wishes to read, it ordinarily takes a relatively large 
amount of time to get to the beginning of it. Thereafter, 
one can move from word to word within the passage at 
a rapid rate. That is to say, initial access time is ordinarily 
much longer than intraserial access time. That is the case 
for several kinds of computer memory, for example, mag- 
netic tapes, magnetic drums and disks, delay-line memo- 
ries of all types. A few kinds of computer memory, how- 
ever, have only one access time: magnetic-core memo- 
ries, thin-film memories, and certain cryogenic memories. 
They are called “random-access” memories because one 
may jump around from register to register at random 
just as fast as he can jump from a register to its nearest 
neighbor. The access time of widely used random-access 
memories is of the same order as the intraserial access 
time of serial memories, and very much shorter than the 
~ initial access time of serial memories. If the ratio of the 
incidence of initial accesses to the incidence of serial ac- 
cesses is not extremely low, therefore, random-access 
memories offer an important advantage in speed over 
serial memories. In the kind of processing that is required 



to organize the body of knowledge, the incidence of initial 
accesses will be high. It is necessary, therefore, to con- 
sider random-access memories and serial memories sepa- 
rately, keeping it in mind that our purpose may be im- 
possible to accomplish as long as the only very large 
memories are serial memories. 

Fast random-access memories were unknown before 
World War I. A hundred 50-bit words is the largest 
capacity that existed two decades ago. Even as late as 
1952, when the SAGE System* was being designed, it 
was difficult to provide 2000 fast, random-access words 
in a single computer memory, and it took the timely in- 
vention of the magnetic-core memory a decade ago to 
make “semi-automatic air defense” feasible. Now, the 
largest random-access memory holds about 130,000 
words, which is approaching 10° bits. If the technology 
of magnetic thin-film memories is developed during the 
next few years in a way that now seems possible, we may 
have hundred-million-bit “modules,” and several or many 
modules per memory, well before 1970.7 

The brief course of development just summarized does 
not provide a firm base for extrapolation. However, the 
technology of digital memory is not operating near any 
fundamental physical limit, and new departures could 
continue to appear once every decade. The size of the 
largest fast, random-access memory could continue, on 
the average, to double every two years. If memory capac- 
ity were to grow at that rate, it would be possible to put 

* Semi-Automatic Ground Environment System for Air Defense. 

7 Shortly after the text was written, “bulk core” memories, with 18 
million bits per unit, and as many as four units per computer, were 
announced for delivery in 1966. A modern maxim says: “People tend 
to overestimate what can be done in one year and to underestimate 
what can be done in five or ten years.” 



all the solid literature of a subfield of science or technol- 
ogy into a single computer memory in 1985. The corre- 
sponding date for a field would be 1988 or 1989, and for 
all solid science and technology it would be about 1996. 
All this refers to fast, random-access digital memory. 

How fast? There is little basis for expecting a marked 
increase in speed (and consequent decrease in access 
time) in the memories that are specialized toward maxi- 
mizing capacity. Although low-capacity memories may 
become very much faster, only an optimist would hope 
for access shorter than 0.1 microsecond in the memories 
discussed in the preceding paragraphs. 

The serial * memories that are of greatest interest in 
the context of this discussion are disk files and photo- 
graphic memories. In the present state of the art, serial 
memories are much more voluminous than random-ac- 
cess memories. There are now available magnetic disk files 
that will store more than a billion bits. In testimony be- 
fore a committee of the House of Representatives in 
1963, E. R. Piore of I.B.M. said that his company was 
working on a trillion-bit photographic memory. For a 
rough rule, one might say that serial memories are ahead 
of random-access memories in capacity by a factor some- 
what greater than 1000, behind random-access memories 
in initial-access speed by a factor considerably greater 
than 10,000, and almost even with random-access memo- 
ries in speed of intraserial access. Advances in serial-ac- 
cess memory appear to be taking place somewhat more 

* Disk files and some photographic memories —e.g., the “photo- 
scopic disk” — are, from a technical standpoint, not precisely serial; 
rather, they are “cyclic.” However, the distinction is not important to 
the present discussion. Magnetic tapes are serial, but handling tape 
introduces a third kind of access delay. Both access to a randomly 
selected tape and access to a randomly selected segment of a given 
tape are very slow. 



rapidly than advances in random-access memory, but 
extrapolation into the distant future seems even less cer- 
tain. Nevertheless, it is likely that within a few years it 
will be possible to fit the solid text of a subfield of knowl- 
edge into a serial memory. This focuses attention on the 
question, shall we then be able to process the text in a 
significant way, or shall we have to wait until we can at 
any moment achieve fast access to any part of the text? 
Before examining what one should mean by “process- 
ing the text in a significant way,” let us take one more 
look at a technological constraint — the constraint on 
“amount of processing.” In computers of the type that 
are in widespread use today, one processor performs suc- 
cessive operations on the contents of memory. The opera- 
tions correspond to “instructions” selected from a set, 
usually larger than 100. The fastest present-day machines 
execute about a million such instructions per second. 
The most promising technological paths appear to be 
open as far as 10 million, or perhaps even 100 million, 
instructions per second. Moreover, the idea of using 
several or many processors simultaneously — “in paral- 
lel” — is under active exploration and development. 
Thus, one can look forward with reasonable confi- 
dence to a time when it will be possible to perform tens 
or hundreds of millions of operations per second upon the 
corpus of a subfield, or even a field, of the body of 
knowledge. That prospect supports the assumption, set 
forth in the introduction, that our thinking and planning 
need not be, and indeed should not be, limited by literal 
interpretation of the existing technology. Extrapolation, 
however uncertain, suggests that the basic “mechanical” 
constraints will disappear: Although the size of the body 
of knowledge, in linear measure of printed text, is almost 



astronomical (about 100,000,000 miles), although that 
measure is increasing exponentially, and although the 
technology that promises to be most helpful to us in 
mastering knowledge is still young and weak, time 
strongly favors the technology. The technology, too, is 
growing exponentially and its growth factor is perhaps 
10 times as great as the growth factor of the corpus. 
Moreover, the technology is not yet near any fundamental 
physical limits to development. Thus in the present cen- 
tury, we may be technically capable of processing the 
entire body of knowledge in almost any way we can de- 
scribe; possibly in ten years and probably within twenty, 
we shall be able to command machines to “mull over” 
separate subfields of the corpus and organize them for 
our use —if we can define precisely what “mulling” 
should mean and specify the kind of organization we 



Aims, Requirements, Plans, 
and Criteria for Procognitive Systems 

BROADLY SPEAKING, the aims of procognitive systems are 
to promote and facilitate the acquisition, organization, 
and use of knowledge. Let us examine these broad aims, 
and some of the general requirements associated with 
them, before moving on to more specific discussion of 
plans and criteria. 


The acquisition of knowledge — the initial apprehen- 
sion of increments to the fund of knowledge — involves 
the recording and representation of events. It involves also 
a selective activity, directed from within the existing body 
of knowledge, and analyzing and organizing activities re- 
lating the increment to the existing body of knowledge. 



Both the acquisitive and the interpretive aspects are rec- 
ognized, and seen to play strongly interactive roles, in 
“experience” and in “experimentation.” However, al- 
though the interpretive aspects are included within it, the 
acquisitive aspects are largely excluded from the present- 
day concept of library. That is to say, when a library 
acquires an increment to its holding, it acquires the incre- 
ment from a publisher, not from “primary nature.” 

The segmentation of the over-all cognitive process ap- 
pears to have arisen, not because it was thought to be 
inherently desirable to turn one’s back on the fund of 
knowledge while seeking out new knowledge to augment 
it, but because there was no way to make, or let, the 
acquisition process interact more directly with the proc- 
esses of organization and maintenance of the main body. 
In thinking about new systems that may not have to suffer 
from that lack, we should keep in mind the possibility of 
developing stronger interactions between the acquisition 
process and the processes that deal with the knowledge 
that already exists. The idea is illustrated schematically 
invEig A. 

To anchor the foregoing general consideration in a 
slightly more specific context, let us consider acquisition 
of knowledge through laboratory experimentation. The 
laboratory and the library are physically separate and dis- 
tinct. The only channels for interaction between them are 
the telephone, the experimenter himself, and the books 
he borrows from the library and examines in the labora- 
tory. The part of the fund of knowledge that interacts 
with nature during an experiment, therefore, is only that 
part that is stored inside the experimenter’s head, plus 
small amounts that come into his head from books he 



reads or from calls he makes to the library while his ex- 
periment is running, or that are implicit in the design of 
his experimental apparatus. Only after he has collected 

(a) (b) 

Fig. 1. (a) Schematic representation of the existing relation be- 
tween acquisition of knowledge through experimentation and the 
library system. “Nature” is represented by N; the body of knowledge 
stored in the library, by K. A small part Ki of K is understood in 
the form of some cognitive structure C,— that is located in the ex- 
perimenter and his laboratory — by an experimenter who conducts an 
experiment 7, upon a small part Ni of N. The three lines con- 
necting one figure with another represent an interaction constrained 
only by the nature of 7:. When the experimenter has collected and 
interpreted his data (not shown), he may write a paper that adds 
something to K:. 

(b) Illustrating the elimination of the constraints and limitations 
imposed by the interposition of the Ci between the 7, and the K of 
diagram a. The experiments may now interact with the whole of K, 
and particularly with all of Ki, using other channels of interaction in 
addition to those provided in diagram a (and now subsumed under 
the broader 7, — K interaction). The advantage of diagram b over 
diagram a depends, of course, upon the effectiveness of the added 
arrangements for interaction. 

and analyzed his data does he go back to the library to 
investigate further their significance in relation to other 
parts of the body of knowledge. Thus the separation of 



library from laboratory forces the use of “batching” pro- 
cedures in the acquisition of knowledge and leads, at 
best, to the collection — in isolation from concurrent 
processes of acquisition, organization, and application — 
of large, monolithic masses of data. At worst, the data 
are collected, not only in isolation from these concurrent 
processes, but also in isolation from one another, and 
the result is a chaos of miscellaneous individual cases. 
The difficulties of integrating the results of many simul- 
taneous research projects that operate with very loose 
linkage to one another and to the body of knowledge is 
at present the object of much concern, particularly in the 
field of pharmaceutical research. 


We have referred repeatedly to “the fund of knowl- 
edge,” “the body of knowledge,” and “the corpus.” The 
most concrete schemata that are useful in shaping the 
concepts associated with those terms are the schemata 
that represent the strings of alphanumeric characters, and 
the associated diagrams, graphs, pictures, and so forth, 
that make up the documents that are preserved in recog- 
nized repositories. However, such simple, concrete sche- 
mata are not in themselves sufficient. Neuroanatomy and 
neurophysiology, together with human behavior, provide 
less definite, but nevertheless necessary, supplementary 
schemata that enrich the concept. These complex ar- 
rangements of neuronal elements and processes accept 
diverse stimuli, including spoken and printed sentences, 
and somehow process and store them in ways that sup- 
port the drawing of inferences and the answering of 



questions; and though these responses are often imprecise, 
they are usually more appropriate to actual demands than 
mere reinstatement of past inputs could ever hope to be. 

When we speak of organizing information into knowl- 
edge, we assume a set of concepts that involves many such 
schemata. The raw materials or inputs to the “organizer” 
are alphanumeric data, geometrical patterns, pictures, 
time functions, and the like. The outputs of the organized 
system are expressed in one or more of the input forms, 
but they are not mere reproductions or translations of 
particular inputs; they are suggestions, answers to ques- 
tions, and made-to-order summaries of the kind that a 
good human assistant might prepare if he had a larger 
and more accurate memory and could process informa- 
tion faster. Concepts of the organizing process, and of the 
organization itself, are the objects of several of the studies 
that will be summarized in later pages. 

In organizing knowledge, just as in acquiring knowl- 
edge, it would seem desirable to bring to bear upon the 
task the whole corpus, all at one time — or at any rate 
larger parts of it than fall within the bounds of any one 
man’s understanding. This aim seems to call for direct 
interactions among various parts of the body of knowl- 
edge, and thus to support the requirement, suggested in 
the Introduction, for an active or directly processible 

One part of the concept of organization, called “mem- 
ory organization,” deals with the design of memory struc- 
tures and systems, as distinct from structures and systems 
of information or knowledge. Its aim is to achieve two 
resonances or congruences: (1) between the memory and 
the information patterns that are likely to be stored in it, 




and (2) between the memory and the requests (e.g., 
- questions) that are likely to be directed to it. 


Knowledge is used in directing the further advance- 
ment and organization of knowledge, in guiding the de- 
velopment of technology, and in carrying out most of the 
activities of the arts and the professions and of business, 
industry, and government. That is to say, the fund of 
knowledge finds almost continual and universal applica- 
tion. Its recursive applications have been mentioned under 
the headings, Acquisition of Knowledge and Organization 
of Knowledge. They require more direct lines of informa- 
tion flow than are now available, lines that may be con- 
trolled by, but do not flow exclusively through, human 

This same need seems even stronger and more evident 
in some of the nonrecursive uses — external applications 
— of knowledge, particularly in engineering. It should be 
possible, for example, to transfer an entire system of 
chemical formulas directly from the general fund of 
knowledge to a chemical process-control system, and to 
do so under human monitorship but not through human 
reading and key pressing. It should be possible for the 
logistics manager who wants to have in his “data base” the 
dimensions of the harbors of the world to connect his own 
information system, through a suitable retrieval filter, 
to the “Procognitive System of Congress.” He should not 
have to assign a dozen employees to a week of searching, 
note taking, and card punching. 

In general, as Fig. 2 suggests, it should be possible to 




Fig. 2. (a) Simplified schematic representation illustrating the flow 
of information in present-day applications of the fund of knowledge 
K. Two applications, A: and A:, are represented, each made by a 
human being H:; working mainly through an application system S:. 
The thickness of the lines represents the amount of information flow. 
All the information flows through the human beings. 

(b) The situation that would prevail if, through the development 
of a procognitive system, the fund of knowledge were extended into 
intimate interactions (represented by the flared projections and their 
interfaces) with human users and their application systems. The 
dotted lines are control paths. Small amounts of control information 
are capable of directing the selection, transformation, and transmis- 
sion of large amounts of substantive information. The human beings 
now function mainly as executives rather than mainly as relayers of 

For complex applications involving several or many men, schema b 
should be extended, of course, to provide communication and co- 
ordination through S: and to let upper echelons exert control over 
lower-echelon channels. 



transfer, directly from the general fund to the mechanism 
of a specific application, the various complexes or repre- 
sentations of knowledge required to support applications. 
The transfer should be requested and controlled through 
a process involving initial prescription, negotiated refine- 
ment of description, tests against various stated criteria, 
and human monitorship. To develop that general ap- 
proach to application should be one of the main aims for 
procognitive systems. 


In each of the three areas, acquisition, organization, 
and application, we are now greatly limited by the con- 
straint that, whenever information flows into, within, or 
out of the main store of knowledge, it must pass through 
people. We shall not belabor the severity of the constraint. 
It is enough to note that a man, reading eight hours a 
day every work day, at a speed appropriate for novels, 
could just keep up with new “solid” contributions to a 
subfield of science or technology. It no longer seems likely 
that we can organize or distill or exploit the corpus by 
passing large parts of it through human brains. It is both 
our hypothesis and our conviction that people can handle 
the major part of their interaction with the fund of knowl- 
edge better by controlling and monitoring the processing 
of information than by handling all the detail directly 

In order even to test the control-and-monitor approach, 
it is necessary first to externalize and make explicit the 
main procedures people employ — together with other 
procedures of equal or greater effectiveness — for deal- 



ing with stored information. It would doubtless be ex- 
tremely difficult to accomplish that preliminary step if 
we included, among the main procedures, complete 
processes leading to insight and discovery. Eventually 
men may succeed in describing those “intelligent” proc- 
esses completely and explicitly. If they do, we should 
like to incorporate the procedures into procognitive sys- 
tems. However, the concept here under discussion does 
not depend upon complete programs for processes of such 
high sophistication. We are thinking in terms of lower- 
echelon procedures. The idea is merely to let people 
control the processing of the information in the body of 
knowledge by (1) applying named sequences or named 
hierarchal arrangements of procedures to named texts, 
graphs, and tables, (2) observing the results, and (3) 
intervening whenever a change or extension of plan is 

We envision several different levels of abstraction in 
the control system and in its languages. At a procedure- 
oriented level, the system would be capable of imple- 
menting instructions such as the following: 

1. Limit domain A in subsequent processing to para- 
graphs that contain at least four words of list x or their 
synonyms in thesaurus y. 

2. Transform all the sentences of document B to kernel 

3. Search domain C for instances of the form u = 
v(w) or w= v’(u) in which uw and w are any names, v 
is any function name in Jist z, and v’ appears in list z as 
the inverse of v. 

4. If the information that meets the prescription can 
be displayed in three pages, display it now; otherwise dis- 
play the number of pages required. 



5. Select from domain D and add to list t each sen- 
tence that deals in any way with an operation upon some- 
thing that contains, or can contain, something else that 
is mentioned in the sentence. 

6. How many documents in the entire store have sec- 
tions characterized by g profiles that correlate above 0.7 
with the g profile of section 3 of document E? 

7. Change 0.7 in the foregoing to 0.8. How many? 

In the foregoing example of instructions in a hypo- 
thetical procedure-oriented language, each term in italics 
is to be regarded as a particular value of a variable; other 
terms of the suggested class would be equally admissible. 
Terms such as “limit . . . to,” “domain,” “subsequent,” 
“processing,” “contain,” “at least,” “of, -their,* “on,” 
“synonym,” “in,” and “transform,” would have standard 
meanings within the system. There would be very many 
such terms. Only specialists would learn all the terms and 
their specific meanings. However, the language would 
offer some flexibility in the use of synonyms and much 
flexibility in selection of syntactic forms, and it would 
not take many months to become a specialist. Instruction 
1, for example, could equally well be given as: 

la. Exclude henceforth from domain A all paragraphs 
not containing four or more words that are in list x or that 
are thesaurus-y synonyms of words that are in list x. 

To devise and implement such a language — successful 
use of which demands substantive knowledge and clear 
thinking, but not rigid adherence to complex rules of 
format — will require an extrapolation, but an achieva- 
ble extrapolation, of computer-programming languages. 

With the aid of the language and procedures suggested 
in the preceding discussion, one could move onward to 



specialized languages, oriented toward particular fields 
or subfields of knowledge, that would be easier to learn 
and use. A servomechanisms engineer, for example, might 
employ a language in which instructions such as the fol- 
lowing could be implemented: 

1. Convert all the Nyquist diagrams in set A to Bode 

2. How many reports are there that contain transfer 
functions of human operators in nonlinear control sys- 

3. How many of the transfer functions are for stochas- 
tic inputs? 

4. Display the transfer functions one at a time on the 

5. Transfer W. E. Smith’s AJAX simulation to my 
Experiment C data base as simulation 2. 

Obviously such a system must contain much substan- 
tive knowledge of its fields. A language for servo engi- 
neers will have to be developed in large part by servo 
engineers. Indeed, the only way to bring into being the 
many field-oriented languages required to support wide- 
spread use of procognitive systems will be (1) to attract 
leading members of the various substantive fields into 
pioneering work in the regions of overlap between the 
substantive fields and the information sciences, and (2) 
to provide them with ready-made component procedures, 
procedure-oriented languages designed to facilitate the 
development of field-oriented languages, and machines 
capable of putting the field-oriented languages to work 
and thus facilitating substantive research and application 
_as soon as the languages are developed. 

7 In any event, a basic part of the over-all aim for pro- 



cognitive systems is to get the user of the fund of knowl- 
edge into something more nearly like an executive’s or 
commander’s position. He will still read and think and, 
hopefully, have insights and make discoveries, but he will 
not have to do all the searching himself nor all the trans- 
forming, nor all the testing for matching or compatibility 
that is involved in creative use of knowledge. He will say 
what operations he wants performed upon what parts of 
the body of knowledge, he will see whether the result 
makes sense, and then he will decide what to have done 
next. Some of his work will involve simultaneous inter- 
action with colleagues and with the fund of stored knowl- 
edge. Nothing he does and nothing they do will impair 
the usefulness of the fund to others.* Hopefully, much 
that one user does in his interaction with the fund will 
make it more valuable to others. 


The set of criteria that should or must be met in the 
design and development of procognitive systems includes 
economic elements and elements relating to technical 
feasibility as well as elements reflecting the needs and 
desires of potential users. It includes also some elements 
that will be governed mainly by quasi-philosophical at- 
titudes toward courses to be followed and goals to be 
sought by man and civilization. Finally, it includes the 
consideration that there must be a way “to get there from 
here,” whether the course be evolutionary (the expressed 

* Except, of course, for the introduction of false information into 
the authenticated and organized core of the fund — but the procogni- 
tive system will be better protected than the present system is against 
the introduction of false information, because of its more elaborate 
editing, correlating, and organizing procedures. 



preference of many present-day system technologists) or 

Economic criteria tend to be dominant in our society. 
The economic value of information and knowledge is 
increasing. By the year 2000, information and knowledge 
may be as important as mobility. We are assuming that 
the average man of that year may make a capital invest- 
ment in an “intermedium” or “console” — his intellectual 
Ford or Cadillac —— comparable to the investment he 
makes now in an automobile, or that he will rent one from 
a public utility that handles information processing as 
Consolidated Edison handles electric power. In business, 
government, and education, the concept of “desk” may 
have changed from passive to active: a desk may be pri- 
marily a display-and-control station in a telecommunica- 
tion-telecomputation system* — and its most vital part 
may be the cable (“umbilical cord”) that connects it, via 
a wall socket, into the procognitive utility net. Thus our 
economic assumption is that interaction with information 
and knowledge will constitute 10 or 20 per cent of the 
total effort of the society, and the rational economic (or 
socioeconomic) criterion is that the society be more pro- 
ductive or more effective with procognitive systems than 

Note that the allocation of resources to information 
systems in this projection covers interaction with bodies 
of information other than the body of knowledge now 
associated with libraries. The parts of the allocation that 
pay for user stations, for telecommunication, and for 
telecomputation can be charged in large part to the han- 

*Tf a man wishes to get away from it all and think in peace and 
quiet, he will have merely to turn off the power. However, it may 
not be economically feasible for his employer to pay him at full rate 
for the time he thus spends in unamplified cerebration. 



dling of everyday business, industrial, government, and 
professional information, and perhaps also to news, en- 
tertainment, and education. These more mundane activi- 
ties will require extensive facilities, and parts of the neo- 
library procognitive system may ride on their coattails. 

Whether or not, even with such help, the procognitive 
system can satisfy the economic criterion within our time 
scale depends heavily upon the future courses of our 
technology and our social philosophy. As indicated 
earlier, the technological prospect can be viewed only 
through uncertain speculation, but the prospect is fairly 
bright if the main trends of the information technology 
hold. The same cannot be said for the philosophical pros- 
pect because it is not as clear what the trends are. 

To some extent, of course, the severity of the criteria 
that procognitive systems will be forced to meet will de- 
pend upon whether the pro- or anti-intellectual forces in 
our society prevail. It seems unlikely that widespread 
support for the development of procognitive systems will 
stem from appreciation of “the challenge to mankind,” 
however powerful that appreciation may be in support 
of space efforts. The facts that information-processing 
systems lack the sex-symbolizing and attention-compel- 
ling attributes of rockets, that information is abstract 
whereas the planets and stars are concrete, and that pro- 
cognitive systems may be misinterpreted as rivaling man 
instead of helping him — these facts may engender in- 
difference or even hostility instead of support. 

At the present time, in any event, not many people 
seem to be interested in intimate interaction with the fund 
of knowledge — but, of course, not many have any idea 
what such interaction would be like. Indeed, it would 
not be like anything in common experience. The only 



widespread schemata that are relevant at all are those de- 
rived from schooling, and they suffer from lack of rele- 
vance on precisely the critical point, intimacy of inter- 
action. The few who do have somewhat appropriate 
schemata for projection of the picture — who have had 
the opportunity to interact intimately (“on line” in a 
good, flexible system) with a computer and its programs 
and data — are excited about the prospect and eager to 
move into the procognitive future, but they are indeed 
few. Even if their number should grow as rapidly as 
opportunity for on-line interaction will permit, they will 
constitute a cadre of useful specialists rather than a broad 
community of eager supporters. 

The foregoing considerations suggest that the economic 
criterion will be rigidly enforced, that procognitive sys- 
tems will have to prove their value in dollars before they 
will find widespread demand. If so, procognitive systems 
will come into being gradually, first in the richest, densest 
areas of application, which will be found mainly in gov- 
ernment and business, and only later in areas in which 
the store of information is poor or dilute. Close inter- 
action with the general fund of knowledge, which is on 
the whole neither rich nor dense, will be deferred, if 
these assumptions are correct, until developments paid 
for by special procognitive applications have made the 
broader effort practicable. Such a “coattail” ride on a 
piecemeal carrier may not be the best approach for the 
nation or the society as a whole, but it seems to be the 
most probable one. In any event, it is beyond the present 
scope to determine an optimal course through the quasi- 
philosophical and socioeconomic waters. 

The criteria that are clearly within our scope are those 
that pertain to the needs and desires of users. The main 



criteria in that group appear to be that the procognitive 

1. Be available when and where needed. 

2. Handle both documents and facts. * 

3. Permit several different categories of input, rang- 
ing from authority-approved formal contributions (e.g., 
papers accepted by recognized journals) to informal notes 
and comments. 

4, Make available a body of knowledge that is organ- 
ized both broadly and deeply — and foster the improve- 
ment of such organization through use. 

5. Facilitate its own further development by pro- 
viding tool-building languages and techniques to users and 
preserving the tools they devise and by recording meas- 
ures of its own performance and adapting in such a way 
as to maximize the measures. 

6. Provide access to the body of knowledge through 
convenient procedure-oriented and field-oriented lan- 

7. Converse or negotiate with the user while he 
formulates his requests and while responding to them. 

8. Adjust itself to the level of sophistication of the 
individual user, providing terse, streamlined modes for 
experienced users working in their fields of expertness, 
and functioning as a teaching machine to guide and im- 
prove the efforts of neophytes. 

9. Permit users to deal either with metainformation 
(through which they can work “at arms length” with 

* “Facts,” used here in a broad sense, refers to items of informa- 
tion or knowledge derived from one or more documents and not con- 
strained to the form or forms of the source passages. It refers also 

to items of information or knowledge in systems or subsystems that 
do not admit subdivision into documentlike units. 



substantive information), or with substantive informa- 
tion (directly), or with both at once. 

10. Provide the flexibility, legibility, and convenience 
of the printed page at input and output and, at the same 
time, the dynamic quality and immediate responsiveness 
of the oscilloscope screen and light pen. 

11. Facilitate joint contribution to and use of knowl- 
edge by several or many co-workers. 

12. Present flexible, wide-band interfaces to other sys- 
tems, such as research systems in laboratories, informa- 
tion-acquisition systems in government, and application 
systems in business and industry. 

13. Reduce markedly the difficulties now caused by 
the diversity of publication languages, terminologies, and 

14. Essentially eliminate publication lag. 

15. Tend toward consolidation and purification of 
knowledge instead of, or as well as, toward progressive 
growth and unresolved equivocation.* 

16. Evidence neither the ponderousness now associ- 
ated with overcentralization nor the confusing diversity 
and provinciality now associated with highly distributed 
systems. (The user is presumably indifferent to the de- 
sign decisions through which this is accomplished. ) 

17. Display desired degree of initiative, together with 
good selectivity, in dissemination of recently acquired and 
“newly needed” knowledge. 

To the foregoing criteria, it may be fair to add criteria 
that are now appreciated more directly by librarians 
than by the users of libraries. Some of the following cri- 

* It may be desirable to preserve, in a secondary or tertiary store, 
many contributions that do not qualify as “solid” material for the 
highly organized, rapidly accessible nucleus of the body of knowledge. 



teria are, as they should be, largely implicit in the fore- 
going list, but it will do no harm to make them explicit. 

18. Systematize and expedite the cataloguing and in- 
dexing* of new acquisitions, forcing conformity to the 
system’s cataloguing standards at the time of “publica- 
tion” and distributing throughout the system the fruits of 
all labor devoted to indexing and other aspects of or- 

19. Solve the problem of (mainly by eliminating) re- 
covery of documents. 

20. Keep track of users’ interests and needs and imple- 
ment acquisition and retention policy (policy governing 
what to hold in local memories) for each local subsystem. 

21. Record all chargeable uses, and handle bookkeep- 
ing and billing. Also record all charges that the system 
itself incurs, and handle their bookkeeping and payment. 

22. Provide special facilities (languages, processors, 
displays) for use by system specialists and by teams made 
up of system and substantive specialists in their con- 
tinual efforts to improve the organization of the fund of 
knowledge. (This professional, system-oriented work on 
organization is supplemented by the contributions toward 
organization made by ordinary users in the course of 
their substantive interaction with the body of knowledge. ) 

23. Provide special administrative and judicial facili- 
ties (again languages, processors, displays) for use in 
arriving at and implementing decisions that affect over- 
all system policies and rules. 

The list of criteria ends with two considerations that 

* “Indexing” is subsumed under “organization” in our use of the 
latter term in connection with documents or corpora. 



we think many users will deem extremely important in 
a decade or two, but few would mention now: 

24. Handle formal procedures (computer programs, 
subroutines, and so forth, written in formal, machine- 
independent languages) as well as the conventional docu- 
ments and facts mentioned in criterion 2. 

25. Handle heuristics (guidelines, strategies, tactics, 
and rules of thumb intended to expedite solution of prob- 
lems) coded in such a way as to facilitate their associa- 
tion with situations to which they are germane. 

The foregoing criteria are set forth, we recognize, es- 
sentially as absolute desiderata, and not — as system cri- 
teria should be — as scales of measurement with relative 
weights, interdependent cutoff points, or other parapher- 
nalia for use in optimization. The reason for stopping so 
far short of an explicit decision procedure is partly that it 
is very difficult to set up such a procedure for so complex 
a system, but mainly that it is too early to foresee the 
details of interaction among the decision factors. The 
foregoing lists are intended not to provide a complete 
mechanism for the evaluation of plans, but merely to 
invite discussion and emendation and to furnish a con- 
text for examination of the “plan” that follows. 


The plan to be presented here is not a plan to be im- 
plemented by a single organization. It is not a system de- 
sign or a management plan. Rather, it is a rough outline 
of researches and developments, many of which will 
probably be carried out, plan or no plan, during the next 



several decades. The reason for setting forth such a plan 
is not to guide research and development, which would 
be presumptuous, but to provide a kind of checklist or 
scorecard for use in following the game. If the technology 
should take care of most of the items in the plan but fall 
behind on a few, then it might be worth while for an 
agency interested in the outcome to foster special efforts 
on the delinquent items. 

Moreover, this plan is not a final plan or even a mature 
plan. Perhaps it should be regarded only as a set of sug- 
gestions, made by a small group without expertness in 
all the potentially contributory disciplines, toward the 
formulation of a plan for a system to facilitate man’s in- 
teraction with the store of knowledge. For the sake of 
brevity, however, let us call it a plan. It will be convenient 
to discuss it in two parts: 

1. The structure and functions of the proposed sys- 

2. Approaches to realization of the proposed system 
through research, technology development, and system 

Structure and functions of proposed system 

The proposed procognitive system has a hierarchical 
structure of the kind mentioned earlier: system, subsys- 
tem, . . . component. It seems at first glance to be 
hierarchical also in another way: it has a top-echelon or 
central subsystem, several second-echelon or regional sub- 
systems, many third-echelon or local subsystems, and 
very many fourth-echelon subsystems or user stations. 
Actually, however, as Fig. 3 illustrates, there are de- 
partures from the simple, treelike paradigm of a true 
hierarchy. First, for the sake of reliability and what the 



military calls “survivability,” the top-echelon subsystem 
should be replicated. However, it may not be possible, or 


Fig. 3. Over-all structure of the procognitive system. The circles and 
ellipses represent advanced and specialized computer systems. The 
squares represent man-computer interfaces, those of echelon 4 being 
stations or consoles for substantive users of the system. Most of the 
connections are switchable telecommunication links. Those shown as 
solid lines represent connections that might be established at a particu- 
lar moment during operation. The dotted lines are introduced to sug- 
gest other connections that could be established. 

The centers of echelon 1 are concerned primarily with maintaining 
the total fund of knowledge, those of echelon 2 with organizing the 
corpora of fields or subfields of knowledge, and those of echelon 3 
with the processing required by users in various localities. The user 
stations of echelon 4 provide input and output (control and display) 
facilities and perhaps some processing and memory associated with 
control and display. 

Except in echelon 1, the number of subsystems envisioned for the 
projected system is very much greater than the number shown. 

even desirable, to give the replicates all the capabilities 
of the main subsystem. Second, each third-level subsys- 
tem may be connected to any higher-level subsystem, 



and to more than one higher-level subsystem at a time. 
Technically speaking, that makes the structure a lattice 
instead of a hierarchy. Perhaps it will be best to call it 
simply a “network.” 

The best schema available for thinking about the third 
and fourth echelons is provided by the multiple-console 
time-sharing computer systems recently developed, or 
under development, at Massachusetts Institute of Tech- 
nology, Cargenie Institute of Technology, System Devel- 
opment Corporation, RAND Corporation, Bolt Beranek 
and Newman, and a few other places. In order to provide 
a good model, it is necessary to borrow features from 
the various time-sharing systems and assemble them into 
a composite schema. Note that the fourth-echelon sub- 
systems are user stations and that the third-echelon sub- 
systems are intended primarily to provide short-term 
storage and processing capability to local users, not to 
serve as long-term repositories. 

The second-echelon subsystems are structurally more 
like computer systems than libraries or documentation 
centers, though they function more like libraries. A typi- 
cal second-echelon subsystem is essentially a digital com- 
puter* with many processors, memory blocks, and input- 
output units working in parallel and with a large and ad- 
vanced memory hierarchy, plus a sophisticated digital 

*It is possible that, before operationally significant procognitive 
systems are developed, another kind of information processor will 
displace, from its prime position in information technology, what we 
now recognize as the digital computer. It seems to us unlikely that 
devices of the perceptron type will best fulfill the purposes with which 
we are here concerned, but other schemata exist and still more are 
conceivable, and plenty of time remains for us to be openminded. 
In any event, the design of digital computers is departing from the 
Princeton paradigm, and the next decade may see as much diversity 
of structure among digital computers as the last decade saw homo- 



communication terminal and stations for use by its own 
specialists in operating and in organizing. Each second- 
echelon subsystem handles one or more than one substan- 
tive field or subfield * of knowledge. Two or three sub- 
systems may work partly in parallel and partly in comple- 
ment in the largest and most active fields or subfields. 

The top-echelon subsystems are similar in general 
schema to the second-echelon subsystems. The top eche- 
lon is specialized (1) to preserve the body of knowledge, 
(2) to add to it progressively the distilled contributions 
received from second-echelon subsystems, (3) to transfer 
information to lower-echelon subsystems on request, and 
(4) to improve the organization of the over-all fund in 
ways complementary to those pursued in the second- 
echelon subsystems. 

The top-echelon memory is, therefore, extremely large. 
Its design may have to sacrifice speed to achieve the neces- 
sary size. For several decades, indeed, it seems likely that 
the limitations on memory size will completely dominate 
the picture, and that there will be little hope of achieving 
a strongly interpenetrating organization of the over-all 
body of knowledge. In the interim, the top echelon will 
be limited essentially to the first three functions. 

Until the top echelon can take up function (4) effec- 
tively, it may be desirable to “organize around the prob- 
lem” in the following way: Use the top echelon, in the 
manner described, to fulfill the first three functions. Cre- 
ate several special second-echelon subsystems to deal 
with cross-field interactions, limiting them to fields (or 
subfields) that are judged likely to have important over- 
laps or significant interconnections. These special second- 

* At first, it will be possible only to handle subfields. As technology 

advances, it may become possible to bring related subfields together 
and to handle an entire field of knowledge in a single subsystem. 



echelon subsystems may not be able to operate on the 
entire corpora of the fields or subfields with which they 
are concerned; they may have to use highly distilled 
representations. Even with such limitations, however, they 
should be able to make valuable contributions by foster- 
ing homogeneity of practice from field to field, detecting 
apparent duplications and complementations in related 
fields, and noting similarities of form or structure in 
models or other information structures employed in sub- 
stantively diverse areas. 

The number of centers in echelon 1 envisioned for a 
national * system is approximately three, as shown in 
Fig. 3. In echelon 2, the number of centers should cor- 
respond roughly to the number of fields (approximately 
100) or subfields (approximately 1000) into which 
knowledge is subdivided for deep analysis and organiza- 
tion. In echelon 3, the number of centers should cor- 
respond to the number of localities in which significant 
interaction with the body of knowledge occurs. “Locali- 
ties” will be large areas if the economic advantage of 
large information-processing systems over small ones 
tends to outweigh the incremental cost (associated with 
the greater distances in larger areas) of communication 
between user stations and centers; they will be small if 
communication costs tend to dominate. Large organiza- 
tions may maintain their own third-echelon centers and 
use them in processing proprietary information as well as 
information from or for the general fund. And the num- 
ber of third-echelon centers will, of course, depend upon 
the demand. These considerations make projection of 

* This discussion is focused on a system appropriate for the United 
States or perhaps for North America. The ways in which the struc- 
ture of a world-wide system would differ depend critically on the 
future economics of intercontinental telecommunication. 



the number of third-echelon centers highly uncertain. It 
falls somewhere between 20 and 2000. We have already 
examined some aspects of the fourth-echelon user sta- 
tions. There will be hundreds of thousands of user sta- 
tions, though many of them will be used only intermit- 

Ordinarily, a user will dial his own nearby third-eche- 
lon center and use its processing and memory facilities. 
His center will probably be holding some of his personal 
data or procedures in its store, and, in addition, using the 
local center will keep down the transmission costs. How- 
ever, when a user wishes to work with a distant colleague, 
and to pool his personal data with those of his colleague, 
he can dial the remote center and request transmission of 
his data to it.* 

A hypothetical example of use of the procognitive system 

Perhaps the best way to consolidate the picture that 
we have been projecting, one part at a time, is to describe 
a series of interactions, between the system and a user 
who is working on a substantive problem that requires 
access to, and manipulation of, the fund of knowledge. 
Let us choose an example that will exercise the system 
in several ways — and try to compensate for the complex- 
ity thus necessarily introduced by describing the interac- 
tion in detail only in the first episode, and then moving 
to a higher level of abstraction. Let us, for the sake of 
brevity, refer to the system as “system” and to the user as 
“I.” And, finally, let us use in the example a fairly 
straightforward descriptor-based approach to document 

* Other arrangements for cooperative work may prove superior to 
the one suggested. Our purpose here is merely to note that the need 
will exist and can be met. 



retrieval, even though that facet of the art should be 
greatly advanced by 1994, and even though we shall not 
hesitate in the same example to assume a question-answer- 
ing capability that is much farther advanced than the 
document-retrieval capability. 

Friday afternoon —I am becoming interested, let us 
say, in the prospect that digital computers can be pro- 
grammed in such a way as to “understand” passages of 
natural language. (That is a 1964 problem, but let us 
imagine that I have available in 1964 the procognitive 
system of 1994.) In preparation for a session of study on 
Monday, I sit down at my console to place an advance 
order for study materials. I take this foresighted approach 
because I am not confident that the subject matter has 
been organized well in the store of the procognitive sys- 
tem, or even that the material I wish to examine is all in 
one subfield center. 

Immediately before me on my console is a typewriter 
that is, in its essentials, quite like a 1964 office typewriter 
except that there is no direct connection between the 
keyboard and the marking unit. When I press a key, a 
code goes into the system, and the system then sends 
back a code (which may or may not be the one I sent), 
and the system’s code activates the marking unit. To the 
right of typewriter, and so disposed that I can get into 
position to write on it comfortably if I rotate my chair a 
bit, is an input-output screen, a flat surface 11” * 14” on 
which the system and I can print, write, and draw to each 
- other. It is easy to move this surface to a position above 
the typewriter for easy viewing while I type, but, because 
I like to write and draw my inputs to the system, I usually 
leave the screen in its horizontal position beside the type- 



writer. In a penholder beside the screen is a pen that can 
mark on the screen very much as an ordinary pen marks 
on paper, except that there is an “erase” mode. The co- 
ordinates of each point of each line marked on the screen 
are sensed by the system. The system then “recognizes” 
and interprets the marks. Inside the console is a camera- 
projector focused upon the screen. Above the chair is a 
microphone. The system has a fair ability to recognize 
speech sounds, and it has a working vocabulary that con- 
tains many convenient control words. Unfortunately, 
however, my microphone is out of order. There is a power 
switch, a microphone switch, a camera button, and a pro- 
jector button. That is all. The console is not one of the 
high-status models with several viewing screens, a page 
printer, and spoken output. 

The power is on, but I have not yet been in interaction 
with the system. I therefore press a typewriter key — any 
key — to let the system know the station is going into 
operation. The system types back, and simultaneously 
displays upon the screen: 

14:23 135 November 1964 

Are you J. C. R. Licklider? 

(The system knows that I am the most frequent, but not 
the only, user of this console.) I type “y” for yes, and the 
system, to provide a neat record on the right-hand side 
of the page, types: 

J Gk. iow Gk taer 

and makes a carriage return. (When the system types, 
the typewriter operates very rapidly; it typed my name in 
a fifth of a second.) The display on the screen has now 
lost the “Are you . . .” and shows only the date and 



name. Incidentally, the typing that originates with me 
always appears in red; what originates in the computer 
always appears in black. 

At this early stage of the proceedings, I am interacting 
with the local center, but the local center is also a sub- 
system of systems other than the procognitive system. 
Since I wish to use the procognitive system, I type 

and receive the reply: 
You are now in the Procognitive System. 

To open the negotiation, I ask the procognitive sys- 

What are your descriptor expressions for: 
computer processing of natural language 
computer processing of English 
natural—-language control of computers 
natural—-language programming of 



At the point at which I wrote “DIGRESS,” it occurred to 
me that I might in a short while be using some of the 
phrases repeatedly, and that it would be convenient to 
define temporary abbreviations. The typed strings were 
appearing on the display screen as well as the paper. (I 
usually leave the console in the mode in which the in- 
formation, when it will fit and not cause delay, is pre- 
sented on both the typewriter and the screen.) I therefore 

_ type: 
define temp 

On recognizing the phrase, which is a frequently used 
control phrase, the system locks my keyboard and takes 
the initiative with: 



define temporarily 
via typewriter? via screen? 

I answer by swiveling to the screen, picking up the pen, 
and pointing to screen on the screen. I then point to the 
beginning and end of computer processing, then to the 
c and the p, and then to a little square on the screen 
labeled “end symbol.” (Several such squares, intended to 
facilitate control by pointing, appear on the screen in 
each mode. ) 

In making the series of designations by pointing just 
described, I took advantage of my knowledge of a con- 
venient format that is available in the mode now active. 
The first two pointings designate the beginning and end of 
the term to be defined, and the next pointings, up to “end 
symbol,” spell out the abbreviation. (Other formats are 
available in the current mode, and still others in other 
modes.) If my microphone had been working, I should 
have said “Define cee pee abbreviation this” and drawn a 
line across computer processing as I said “this.” The sys- 
tem would then have displayed on the screen its interpre- 
tation of the instruction, and then (after waiting a 
moment for me to intervene) implemented it. 

Next, I define abbreviations for “natural language” 
(nl), “computer” (comp), and “programming” (prog). 
(Unless instructed otherwise, the system uses the same 
abbreviation for singular and plural forms and for hy- 
phenated and unhyphenated forms.) And finally, insofar 
as this digression is concerned, I touch a small square 
on the screen labeled “end digression,” return to the type- 
writer, and type: 

comp understanding of nl 
comp comprehension of semantic relations?§ 



The question mark terminates the query, and the symbol 
§ tells the system not to wait for further input from me 

Because the system’s over-all thesaurus is very large, 
and since I did not specify any particular field or subfield 
of knowledge, I have to wait while the requested infor- 
mation is derived from tertiary memory. That takes about 
10 seconds. In the interim, the system suggests that I 
indicate what I want left on the display screen. I draw a 
closed line around the date, my name, and the query. The 
line and everything outside it disappear. Shortly there- 
after, the system tells me: 

Response too extensive to fit on screen. Do you wish short 
version, multipage display, or typewriter-only display? 

Being in a better position to type than to point, I type: 


That is enough to identify my preference. The short 
version appears on the screen but not the typewriter — 
I rather expected it there too): 

Descriptor expressions: 
1. (natural language) / (computer processing of) 
2. (natural language) /\ (on-line man-computer interac- 
. (natural language) A (machine translation of) 
. (natural language) /\ (computer programming) 
. (computer program) /\ (semantic net) 
6. (compiler) /\ (semantic) 
Descriptor inclusions: 
7. (natural language) includes (English) 
Phrase equivalences: 

NO kh OO 



8. (control of computers) ~ (on-line man-machine _inter- 

9. (programming of computers) ~ (computer pro- 

10. (semantic relations) ~ (semantic nets) 


I am happy with 1 and especially happy with 2. I am 
curious about 5. I think I should look into 4. Number 3 
frightens me a little, because I suspect it will lead to too 
much semirelevant information. I think I recognize 6 as 
a false lead into the field of program compiling, in which 
field, although “syntax” seems to be used in essentially the 
linguist’s sense, “semantics” is used in what appears to 
me to be an inscrutable kind of jargon. Accordingly, I 
type to the system: 

Prepare bibliographies with abstracts 
ligt 2 ho Ay 

Best 25 references each. When will they 
be available? 

Before turning control over to the system, I decide to ask 
a substantive question, even though I realize I do not 
yet know enough about the subject to inquire intelligently. 
Using the control word, “QUESTION,” to establish the 
mode, I ask: 

QUESTION: Has any digital-—computer system 
responded to questions phrased in ordinary 
English, or other natural language, by 
giving correct answers in natural 
language? § 

The system replies first to “When will they be avail- 



Requested bibliographies will be available at about 18:00. 
Do you want them delivered, or will you call, or will you re- 
quest typeout? 

Unfortunately, my office is not located near a pneumatic- 
tube station. I rely heavily on typeouts made by the sys- 
tem when I am away from the console. I reply: 

Type them out as soon as they are 

The system reminds me: 

Please leave your console power on! 

Then it replies to the “QUESTION”: 

Cannot give yes-no answer to QUESTION. Approaches to “yes” 

are described in the following: 

1. B. F. Green, A. K. Wolf, C. Chomsky, and K. Laughery, 
Baseball: An Automatic Question-Answerer, Proc. Western 
Joint Computer Conf., 19, 219-224, 1961. 

2. S. Klein and R. F. Simmons, A Computational Approach to 
Grammatical Coding of English Words, J. Assoc. Computing 
Machinery, 10, 334-347, 1963. 

3. S. Klein and R. F. Simmons, Syntactic Dependence and the 
Computer Generation of Coherent Discourse, Mechanical 
Translation (entering system). 

The foregoing must suffice to suggest the nature of the 
interaction at the level of key pressing and pointing. The 
console hardware and procedure embody many features, 
worked out through the years, that maximize convenience 
_and free the user from clerical routine. The formats and 
procedures are quite flexible. The user learns, through 
working with the system, what modes and techniques suit 
him best. Ordinarily, he gives the system rather terse, 
almost minimal instructions, relying on it to interpret 
them correctly and to do what he wishes and expects. 



When it misinterprets him or gets off the track of his 
thinking, as it sometimes does, he falls back on more ex- 
plicit expression of commands and queries. 

To continue with our example, let us move on to Mon- 
day. The reference citations and abstracts are ready for 
examination. The system has anticipated that I may want 
to see or process the full texts, and they are now available 
in secondary memory, having been moved up from the 
tertiary store along with a lot of other, somewhat less 
clearly relevant, material. I do not know exactly how 
much of such anticipatory preparation has gone on within 
the system, but I know that the pressure of on-line re- 
quests is low during the week-end, and I am counting on 
the system to have done a fair amount of work on my 
behalf. (I could have explicitly requested preparatory as- 
sembly and organization of relevant material, but it is 
much less expensive to let the system take the initiative. 
The system tends to give me a fairly high priority because 
I often contribute inputs intended to improve its capa- 
bilities.) Actually, the system has been somewhat behind 
schedule in its organization of information in the field of 
my interest, but over the week-end it retrieved over 
10,000 documents, scanned them all for sections rich in 
relevant material, analyzed all the rich sections into state- 
ments in a high-order predicate calculus, and entered the 
statements into the data base of the question-answering 

It may be worthwhile to digress here to suggest how 
the system approached the problem of selecting relevant 
documents. The approach to be described is not ad- 
vanced far beyond the actual state of the art in 1964. 
Certainly, a more sophisticated approach will be feasible 
before 1994. 



All contributions to the system are assigned tentative 
descriptors when the contributions are generated. The 
system maintains an elaborate thesaurus of descriptors and 
related terms and expressions. The thesaurus recognizes 
many different kinds of relations between and among 
terms. It recognizes different meanings of a given term. 
It recognizes logical categories and syntactic categories. 
The system spends much time maintaining and improv- 
ing this thesaurus. As soon as it gets a chance, it makes 
statistical analyses of the text of a new acquisition and 
checks the tentatively assigned descriptors against the 
analyses. It also makes a combined syntactic-semantic 
analysis of the text, and reduces every sentence to a 
(linguistic) canonical form and also to a set of expres- 
sions in a (logical) predicate calculus. If it has serious 
difficulty in doing any of these things, it communicates 
with the author or editor, asking help in solving its prob- 
lem or requesting revision of the text. It tests each unex- 
pectedly frequent term (word or unitary phrase) of the 
text against grammatical and logical criteria to determine 
its appropriateness for use as a descriptive term, draws up 
a set of working descriptors and subdescriptors, sets them 
into a descriptor structure, and, if necessary, updates the 
general thesaurus. * 

In selecting documents that may be relevant to a re- 
trieval prescription, the system first sets up a descriptor 
structure for the prescription. This structure includes all 
the terms of the prescription that are descriptors or sub- 
descriptors at any level in the thesaurus. It includes, also, 
thesaurus descriptors and subdescriptors that are synony- 
mous to, or related in any other definite way to, terms 

* New entries into the general thesaurus are dated. They remain 
tentative until proven through use. 



of the prescription that are not descriptors or subdescrip- 
tors in the thesaurus. All the logical relations and modula- 
tions of the prescription are represented in its descriptor 

The descriptor structure of a document is comparable 
to the descriptor structure of a prescription. The main 
task of the system in screening documents for relevance, 
therefore, is to measure the degrees of correlation or con- 
gruence that exist between various parts of the prescrip- 
tion’s structure and corresponding parts, if they exist, of 
each document’s structure. This is done by an algorithm 
(the object of intensive study during development of the 
system) that determines how much various parts of one 
structure have to be distorted to make them coincide with 
parts of another. The algorithm yields two basic measures 
for each significant coincidence: (1) degree of match, 
and (2) size of matching substructure. The system then 
goes back to determine, for each significant coincidence, 
(3) the amount of text associated with the matching de- 
scriptor structure. All three measures are available to the 
user. Ordinarily, however, he works with a single index, 
which he is free to define in terms of the three measures. 
When the user says “best references,” the system selects on 
the basis of his index. If the user has not defined an index, 
of course, the system defines one for him, knowing his 
fields of interest, who his colleagues are, how they de- 
fined their indexes, and so forth. 

We shall not continue this digression to examine the 
question-answering or other related facilities of the sys- 
tem. Discussions relevant to them are contained in Part II. 
Let us return now to the conclusion of the example. 

I scan the lists of references and read the abstracts. I 
begin to get some ideas about the structure of the field, 




and to appreciate that it is in a fairly primitive stage. Evi- 
dently, it is being explored mainly by linguists, logicians, 
psychologists, and computer scientists, and they do not 
speak a uniform language. My interest is caught most 
strongly by developments in mathematical syntax. The 
bibliography contains references to work by Noam Chom- 
sky, Ida Rhodes, A. G. Oettinger, V. E. Giuliano, V. H. 
Yngve, and others. I see that I was wrong in neglecting 
machine translation. I correct that error right away. The 
requested bibliography appears at once; the system had 
discovered the relevance and was prepared. 

The first thing I wish to clear up is whether the syntac- 
tic and semantic parts of language are, or should be, 
handled separately or in combination in computer analy- 
sis of text. I give the system a series of questions and 
commands that includes: 

Refer to bibliographies I requested last 

Do cited or related references contain 
explicit. definitions of "syntax", 
"syntactics"”, or “semantic"? 

Do syntactic analyses of sentences yield 
many alternative parsings? 

Give examples showing alternatives. Give 
examples illustrating how many. 

Is there a description of a procedure in 
which an analysis based on mathematical 
syntax is used in tandem or in alternation 
with semantic analysis? 

Display the description. 

How long is Oettinger's syntactic 

Do you have it available now? § 

It turns out that Oettinger, Kuno and their colleagues 
have developed a series of syntactic analyzers and that the 
most recent one has been documented and introduced 



into the procognitive system (Oettinger and Kuno, 
1962). I do not have to bother Oettinger himself — at 
least not yet. 
I request that the program be retrieved and prepared 
for execution by asking: 
What arguments and control operations does 
the routine require? What formats? How 
dosLtest it? 
How do I apply the routine to a test 
The system tells me that all I have to do to apply the 
routine to a short test sentence, now that the system has 
the routine all ready to go, is to type the sentence; but 
for long inputs there are rules that I can ask to see. I type 
a sentence discussed by Kuno and Oettinger (1963): 

They are flying planes. 

The result pours forth on the screen in the form of a 
table full of abbreviations. I think I can probably figure 
out what the abbreviations mean, but it irritates me when 
the system uses unexplained abbreviations in a field that 
I am just beginning to study. I ask the system to associate 
the spelled-out terms with the abbreviations in the table. 
It does so, in very fine print, and appends a note citing 
the program write-up that explains the notations. I can 
barely make out the fine print. Partly to make sure of my 
reading, and partly to exercise the system (which still 
has a certain amount of plaything appeal), I touch “1V” 
on the tree diagram with the stylus, and then hold the 
stylus a moment on the control spot labeled “magnify.” 
The tree expands around the “1V,” enlarging the print, 
and thereby lets me confirm my uncertain reading of 
“level one, predicative verb.” 

My next step is to test a sentence of my own. After 



that, I ask to see the other programs in the system that 
are most closely similar in function to the one just ex- 
amined. The system gives me first a list of the names and 
abstracts of several syntax programs, and then as I call 
for them, the write-ups and listings, and it makes each 
program available for testing. I explore the programs, but 
not yet very deeply. I wish merely to gain an impression 
from direct interaction with them and then go back to a 
mixture of reading and asking questions of the system. 

The foregoing is doubtless enough to suggest the nature 
of the interaction with the fund of knowledge that we 
think would be desirable. None of the functions involved 
in the interaction described in the example is very com- 
plex or profound. Almost surely the functions can be 
implemented in software* sooner than the hardware re- 
quired to support them will be available. As the example 
suggests, we believe that useful information-processing 
services can be made available to men without the pro- 
gramming of computers to “think” on their own. We be- 
lieve that much can be accomplished, indeed, without 
demanding many fundamental insights on the part of the 
initial designers of the system. 

Perhaps we did not rely heavily enough, in the example 
and in the study, on truly sophisticated contributions from 
the inanimate components of the system. In respect of 
that possibility, we adopted a deliberately and admittedly 
conservative attitude. We expect that computers will be 
capable of making quite “intelligent” contributions by 
1994, to take the date assumed in the example, but we 

prefer not to count on it. If valuable contributions can 
* Computer programs, descriptions of procedure, dictionaries, in- 
structional material, and so forth, as opposed to hardware, which is 

usually taken to include the processors, memories, display devices, 
communication equipment, and other such components of the system. 



be made by “artificial intelligences” of that date, there 
will be room for them, as well as the men to monitor 
them, in our basic system schema. On the other hand, if 
it should turn out that the problems involved in develop- 
ing significant artificial intelligence are extremely diffi- 
cult, or that society rejects the whole idea of artificial in- 
telligence as a defiance of God or a threat to man, then 
it will be good not to have counted on much help from 
software approaches that are not yet well enough under- 
stood to support extrapolation. This conservative attitude 
seems appropriate for the software area but not for the 
hardware area. 


Our information technology is not yet capable of con- 
structing a significant, practical system of the type we 
have been discussing. If it were generally agreed, as we 
think it should be, that such a system is worth striving 
for, then it would be desirable to have an implementation 
program. The first part of such a program should not con- 
cern itself directly with system development. It should 
foster advancement of relevant sectors of technology. * 

Let us assume then — though without insisting — that 
it is in the interest of society to accelerate the advances. 
What particular things should be done? 

Overcome interdisciplinary barriers 

One of the first things to do, according to our study, is 
to break down the barriers that separate the potentially 

* Science is also involved, of course, but for the sake of brevity 
“technology” is used in a very broad sense in this part of the discus- 



contributory disciplines. Among the disciplines relevant 
to the development of procognitive systems are (1) the 
library sciences, including the part of information storage 
and retrieval associated with the field of documentation, 
(2) the computer sciences, including both hardware and 
software aspects and the part of information storage and 
retrieval associated with computing, (3) the system sci- 
ences, which deal with the whole spectrum of problems 
involved in the design and development of systems, and 
(4) the behavioral and social sciences, parts of which 
are somewhat (and should be more) concerned with how 
people obtain and use information and knowledge. (The 
foregoing is not, of course, an exhaustive list; it even 
omits mathematical linguistics and mathematical logic, 
both of which are fundamental to the analysis and trans- 
formation of recorded knowledge.) The barriers that 
separate the relevant disciplines appear to be strong. 
There is, of course, some multidisciplinary work, and a 
little of it is excellent. On the whole, however, the poten- 
tially contributory disciplines are not effectively con- 
joined. One of the most necessary steps toward realiza- 
tion of procognitive systems is to promote positive inter- 
action among them. 

Develop the concept of relevance network 

A second fundamental step is to determine basic char- 
acteristics of the relevance network that interrelates the 
elements of the fund of knowledge. The information ele- 
ments of a sentence are interrelated by syntactic struc- 
tures and semantic links. The main syntactic structures 
are obviously local; they scarcely span the spaces between 
sentences. Correspondence between syntactic structures is 
of some help in determining the type and degree of rela- 



tion between two widely separated segments of text, but 
the main clues to the relations that interconnect diverse 
parts of the corpus of recorded information are semantic. 

There is, therefore, a need for an effective, formal, 
analytical semantics. With such a tool, one might hope 
to construct a network in which every element of the 
fund of knowledge is connected to every other element 
to which it is significantly related. Each link might be 
thought of as carrying a code identifying the nature of 
the relation. The nature might be analyzed into type and 
degree.* Multiple-argument relations would be repre- 
sented by multiple linkages. We use the term, “relevance 
network,” to stand for this entire concept. 

The magnitude of the task of organizing the corpus of 
recorded information into a coherent body of knowledge 
depends critically upon the average length of the links of 
the relevance network. To develop this idea, let us visual- 
ize the network as a reticulation of linkages connecting 
information elements in documents that are arranged 
spatially in a pattern corresponding to some classification 
system such as the Dewey Decimal. Now let us determine, 
for each element i, the number Ni; of links of each degree 
j that connect it to other elements, and determine, at the 
same time, the total length Li; of all its links of each de- 
gree j. The average length of all the links of degree j in 
the network is 

Lj = (2L5)/(2Nu) 

If we weight the lengths by an inverse function such as 

* Here “degree” implies a formalization of the intuitive notion that 
some relations are direct and immediate (e.g., x is the mother of y) 
whereas others are indirect and mediate (e.g., x is a member of a club 
of the same type as a club of which y is a member). Low degree 
corresponds to direct and immediate. 



1/j? of their degrees, we have as an index for the average 
weighted length of the links: 

1g as =Lj/j’ 

In order to determine the foregoing quantities precisely, 
one would have to carry out much of the task of organiz- 
ing the body of knowledge, but we are concerned here 
mainly with the abstract concept, and sampling experi- 
ments would, in any event, suffice to make it concrete. 

If at the outset we could fit the entire corpus into a 
giant random-access memory, we should not be con- 
cerned with the lengths of links. The total number of 
elements and the total number of links up to some cutoff 
degree would provide the bases for estimating the magni- 
tude of the task of organizing the body of knowledge. 
However, as long as we can fit into processible memory 
only one part of the corpus at a time, it will be critical 
whether the linked elements of the relevance network 
cluster, and whether the memory will accept a typical 
cluster. The index L bears on that question. If L turns 
out to be small, then knowledge does indeed tend to 
cluster strongly, and part-by-part processing of the corpus 
will be effective. If L turns out to be large, then far- 
flung associations are prevalent, and we must await the 
development of a large memory. 

In the foregoing discussion, the index L was based 
upon “lengths” in a space defined to correspond with a 
linear classification scheme. Obviously, that assumption, 
and many other parts of the suggested picture, need to be 
sharpened. One should not adopt the first paradigm to 
come to mind, but should explore the implications of 
various alternative properties and metrics of the relevance 
space. Moreover, one should regard the lengths of links 



and the metrics of the space merely as preliminary work- 
ing conveniences, for all the lengths within a part of the 
corpus become equal when that part is loaded into a 
random-access memory, and the distance of that part 
from the other parts may, for practical purposes, become 
infinite. It is of paramount importance not to think of 
relevance as a vague, unanalyzed relation, but rather to 
try to distinguish among definite types and degrees of rele- 
vance. With such development, the concept of relevance 
networks might progress from its present unelaborated 
form to a systematic, analytic paradigm for organization 
of the body of knowledge. 

Develop advanced memory systems 

The most necessary hardware development appears to 
be in the area of memory, which we have already dis- 
cussed. Procognitive systems will pose requirements for 
very large memories and for advanced memory organiza- 
tions. Unless an unexpected breakthrough reconciles fast 
random access with very large capacity, there will be a 
need for memories that effect various compromises be- 
tween those desiderata. They will comprise the echelons 
of the memory hierarchy we have mentioned. It will be 
necessary to develop techniques for transferring informa- 
tion on demand, and in anticipation of demand, from the 
slow, more voluminous levels of the hierarchy to the 
faster, more readily processible levels. 

Insofar as memory media are concerned, current re- 
search and development present many possibilities. The 
most immediate prospects advanced for primary mem- 
ories are thin magnetic films, coated wires, and cry- 
ogenic films. For the next echelons, there are magnetic 
disks and photographic films and plates. Farther distant 



are thermoplastics and photosensitive crystals. Still farther 
away — almost wholly speculative — are protein mole- 
cules and other quasi-living structures. All these possibil- 
ities will be explored by industry without special prod- 
ding, but it may in some instances be difficult for industry, 
unassisted, to move from demonstrations of feasibility in 
the laboratory into efficient production. 

Associative, or content-addressable, memories are be- 
ginning to make their appearance in the computer tech- 
nology. The first generation is, of course, too small and 
too expensive for applications of the kind we are inter- 
ested in here, but the basic schema seems highly relevant. 
One version of the schema has three kinds of registers: a 
mask register, a comparison register, and many memory 
registers. All the registers have the same capacity except 
that each memory register has a special marker cell not 
found in the mask and comparison registers. The con- 
tents of the mask register are set to designate the part of 
the comparison and memory registers upon which atten- 
tion is to be focused. The comparison and memory regis- 
ters contain patterns. Suppose that “golf” falls within the 
part of the comparison register designated as active by 
the mask. When the “compare” instruction is given, the 
marker is set to 1 in the marker cell of every memory 
register that contains “golf” in the part designated by the 
mask, and the marker is set to 0 in the marker cell of 
every other memory register. This is done almost simul- 
taneously in all the memory registers in one cycle of 
processing. The ordinary, time-consuming procedure of 
searching for matching patterns is thus short-circuited. 

Our earlier discussion of retrieval with the aid of de- 
scriptors and thesauri suggested that searching for match- 
ing patterns is likely to be a prevalent operation in pro- 



cognitive systems. Associative memories are therefore 
likely to be very useful. However, the simple schema just 
described is not capable of handling directly the highly 
complex and derivative associations (e.g., A associated 
with D through B and C if E equals F) that will be en- 
countered. It seems desirable, therefore, to explore more 
advanced associative schemata. These should be studied 
first through simulation on existing computers. Only 
when the relative merits of various associative-memory 
organizations are understood in relation to various infor- 
mation-handling problems, we believe, should actual 
hardware memories be constructed. 

In the body of knowledge, relations of high order ap- 
pear to prevail over simple associations between paired 
elements. That consideration suggests that we should not 
content ourselves with simple associative memories, but 
should press forward in an attempt to understand and 
devise high-order relational memories. 

Develop fast processors consistent 
with advanced memory structure 

Memory, of course, is only part of the picture. With 
each development in memory structure must come a de- 
velopment in processors. For example, now that “list 
processing” has been employed for several years, com- 
puters are appearing on the market with instruction codes 
that implement directly most of the manipulations of list 
structures that were formerly handled indirectly through 
programming. It will be desirable eventually to have 
special instructions for manipulating “relational nets” or 
whatever information structures prove most useful in 
representing and organizing the fund of knowledge. 



Develop advanced displays and controls 
for man-computer interaction 

Some of the projected devices that promise to facilitate 
interaction between men and the body of knowledge were 
described on pp. 45—46. Most of the capabilities that were 
assumed in the example can be demonstrated now, but 
only crudely, and one feature at a time. It will require 
major research and engineering efforts to implement the 
several functions with the required degrees of conven- 
ience, legibility, reliability, and economy. Industry has 
not devoted as much effort to development of devices and 
techniques for on-line man-computer interaction as it has 
to development of other classes of computer hardware 
and software. It seems likely, indeed, that industry will 
require more special prodding and support in the display- 
control area than in the other relevant areas of computer 

Develop procedure-oriented, field-oriented, 
and user-oriented languages 

The design of special-purpose languages is advancing 
rapidly, but it has a long way to go. There are now sev- 
eral procedure-oriented languages for the preparation of 
computer programs (1) to solve scientific problems, (2) 
to process business data, and (3) to handle military in- 
formation. Examples are: (1) ALGOL, FORTRAN, 
CAL; (2) COBOL and FACT; and (3) JOVIAL and 
NELIAC. In addition, there are languages oriented to- 
ward (4) exploitation of list processing, (5) simulation 
techniques, and (6) data bases. Examples are: (4) 



and (6) ADAM, COLINGO, and LUCID. Finally, there 
are languages oriented toward the problems of particu- 
lar fields of research and engineering, for example, 
STRESS and COGO (for civil engineering), and Sketch- 
pad and APT (for mechanical design). 

It will be absolutely necessary, if an effective procogni- 
tive system is ever to be achieved, to have excellent lan- 
guages with which to control processing and application 
of the body of knowledge. There must be at least one 
(and preferably there should be only one) general, pro- 
cedure-oriented language for use by specialists. There 
must be a large number of convenient, compatible field- 
oriented languages for the substantive users. From the 
present point of view, it seems best not to have an inde- 
pendent language for each one of the various processing 
techniques and memory structures that will be employed 
in the system, but to embed all such languages within the 
procedure-oriented and field-oriented languages — as 
SLIP (for list processing) is embedded within FOR- 
TRAN (Weizenbaum, 1963). 

Advance the understanding of machine processing 
of natural languages 

To what extent should the language employed in the 
organization, direction, and use of procognitive systems 
resemble natural languages such as English? That ques- 
tion requires much study. If the answer should be, “Very 
closely,” the implementation will require much research. 
Indeed, much research on computer processing of natural 
language will be required in any event, for the text of the 
existing corpus is largely in the form of natural language, 
and the body of knowledge will almost surely have to be 



converted into some more compact form in the interests 
of economy of storage, convenience of organization, and 
effectiveness of retrieval. 

In the organization of the corpus, moreover, it will 
surely be desirable to be able to translate from one natural 
language to another. Research and development in ma- 
chine translation is, therefore, relevant to our interests. 
At present, students of machine translation seem to be at 
the point of realizing that syntactic analysis and large 
bilingual dictionaries are not enough, that developments 
in the field of semantics must be accomplished before 
smooth and accurate translations can be achieved by 
machine. Thus machine translation faces the same prob- 
lem we face in the attempt, upon which we have touched 
several times, “to organize information into knowledge.” 

There appear to be two promising approaches to the 
rationalization of semantics. The first, which we have al- 
ready mentioned briefly, involves formalization of seman- 
tic relations. The second, not yet mentioned, involves 
(1) the amassing of vast stores of detailed information 
about objects, people, situations, and the use of words, 
and (2) the development of heuristic methods of bring- 
ing the information to bear on the interpretation of text. 
As we see it now, researches along both these approaches 
should be fostered. The first is more likely to lead to com- 
pact representations and economic systems. Perhaps, 
however, only the second will prove capable of handling 
the “softer” half of the existing corpus. 

Develop multiple-access computer systems 

The central role, in procognitive systems, of multiple 
access to large computers was emphasized in an earlier 
section. It seems vitally important to press on with the 



development of multiple-console computer systems, par- 
ticularly in organizations in which creative potential users 
abound. As soon as it is feasible, moreover, multiple- 
console computer systems should be brought into contact 
with libraries. Perhaps they should be connected first to 
the card catalogues. Then they should be used in the de- 
velopment of descriptor-based retrieval systems. Almost 
certainly, the most promising way to develop procognitive 
systems is to foster their evolution from multiple-console 
computer systems — to arrange things in such a way that 
much of the conceptual and software development will 
be carried out by substantive users of the systems. 



Information Storage, 
Organization, and Retrieval 

THE PURPOSE OF this section is to focus briefly on basic 
concepts of the field of “storage and retrieval” that seem 
particularly relevant to procognitive systems. Some of the 
ideas of this field have already been mentioned in our 
example in which documents were retrieved with the aid 
of descriptors and a thesaurus. We have also illustrated 
applications of passage-retrieval and question-answering 
techniques — techniques that penetrate the covers of 
documents and deal with sentences and paragraphs or 
with “ideas” and “facts.” Let us now examine those and 
related techniques a bit more systematically. 

The basic unit of knowledge appropriate to our pur- 
poses may well be akin to the “idea” of popular and 
sometime philosophical usage, but we shall not try to ex- 



ploit that possibility because “idea” is discouragingly 
nebulous. Alternatively, the basic unit may be closely re- 
lated to the mathematical concept of “function,” or to the 
logical concept of “relation” that figured centrally in our 
earlier discussion of relevance networks. Again alterna- 
tively, the logical systems of predicate calculus, particu- 
larly the so-called higher-order predicate calculi, offer 
formalisms for the expression of complex attributes 
(predicates) and attributions (sentences). Some of these 
systems not only provide implementable procedures for 
deduction but also have the advantage of being well de- 
veloped and thoroughly tested. Finally, there is the ap- 
paratus of linguistics, with several syntactic categories 
and myriad rules of grammar. 

Despite the ready availability of the foregoing concepts, 
most of the work that has been done in the field of infor- 
mation storage, Organization, and retrieval has been 
based on the simplest of ideas about sets. The next most 
popular schema, if we count implicit as well as explicit 
application, has been geometric space. We may organize 
Our examinations of this area, therefore, by considering 
storage, Organization, and retrieval systems based on the 
following models: (1) sets and subsets, (2) space ana- 
logues, (3) functions and relations, (4) predicate calcu- 
lus, and (5) other formal languages. 


In most systems based on the ideas of sets and subsets, 
the fundamental concepts are set, partition, item, name, 
term, prescription, storer, organizer, and retriever, and 



the logical connectives. Although details of the concepts 
and the names used in referring to the concepts vary con- 
siderably from system to system, the same fundamental 
ideas appear repeatedly. 

The items are the things to be stored and retrieved: 
documents, facts, and so forth. There is a set of items. 
Each item may or may not have a name. Terms are 
associated with items by being written, usually by storers, 
on the items themselves or on tags or cards associated with 
the items. Prescriptions are made up mainly of terms and 
are usually written by retrievers. For each system, there 
is a rule that determines whether the terms associated with 
a given document sufficiently match those of a given pre- 
scription. The rule and the mechanism for implementing 
it are devised by organizers. The object of retrieval, in 
systems based on sets and subsets, is to partition the set 
of items in such a way as to separate the items a retriever 
desires from those he does not desire. 

In order to establish a perspective, let us examine 
briefly, and somewhat abstractly, some familiar retrieval 

Partitioning by naming 

The very simplest retrieval method achieves the parti- 
tion by naming the elements (or items) of the desired 
subset. That method is not applicable to such items as 
sentences and facts that do not have names. Moreover, 
- when the retriever does not know the names of the items 
he desires, the method does not work even with those 
items that do have names, such as books and journal ar- 
ticles. Nevertheless, the method and the location-coded 
cards often used in implementing it are simple and widely 



Hierarchical indexing 

If the items have no names, or if the names of desired 
items are unknown to the retriever, it is necessary to fall 
back on the use of descriptive terms to specify the desired 
items. In most term-based systems, either it is assumed 
that the retriever knows the terms, or glossaries or thesauri 
listing the legal terms of the system are provided. In a 
hierarchical system, first the over-all set of items is parti- 
tioned by organizers into mutually exclusive and exhaus- 
tive first-echelon subsets or categories, and a unique term 
(sometimes a code digit) is assigned to each. Then each 
first-echelon subset is partitioned into mutually exclusive 
and exhaustive second-echelon subsets or categories, and 
a unique term (or code digit) is assigned to each of them. 
This process of subdivision is continued until there are 
as many echelons as can be handled conveniently or until 
there are only a few items in each subset of the lowest 
echelon. The retriever in this system merely composes a 
prescription consisting of one term for each echelon. He 
makes his way down the branching, rootlike structure of 
the hierarchy, selecting first the first-echelon subset cor- 
responding to the first-echelon term of his prescription, 
then the second-echelon subset corresponding to the 
second-echelon term of his prescription, and so forth. 
When he gets to the bottom, or to a level at which there 
are not too many items, he examines the items of the 
subset he has isolated. In practice, the main trouble with 
this scheme is a trouble inherent in all serial-decision 
methods: one mistake anywhere in the series, and the 
game is lost! Perhaps a more basic difficulty is that knowl- 
edge does not seem to be naturally susceptible to hierar- 
chical analysis. For these reasons, storage and retrieval 
systems that set out to be hierarchical often turn into lat- 



ticelike systems through nonexclusive categorization and 
cross referencing. 

Coordinate indexing 

The difficulties just mentioned can be avoided by giv- 
ing up the notion of precedence that orders the hierarchy. 
Without precedence, all the subsets and all the terms are 
coordinate. In coordinate indexing systems, the organizers 
partition the set of items in various premeditated ways 
and assign a term, not to each subset, but to each parti- 
tion. The term itself identifies one of the two subsets 
separated by the partition — usually the smaller one. (If 
negation is used, the negation of the term identifies the 
other subset.) The retriever then draws up a prescription 
consisting of terms joined by logical connectives. Often 
the logical “and” is the only connective employed. In 
some systems, use is made also of “or” and “not.” The 
mechanism that fulfills the prescription has to find and de- 
liver the subset of items corresponding to the logical ex- 
pression. Given, for example, the prescription: 


the mechanism would retrieve the items characterized by 
A and those characterized by both B and C, and, in addi- 
tion, those characterized by D but not by E. Many com- 
mercial and government systems are based on coordinate 
indexing: e.g., most edge-notched-card systems, the Peek- 
_A-Boo Card system, and the descriptor system of the De- 
fense Documentation Center. 

Inverse filing 

The “natural” or “first-thought” way to set up a co- 
ordinate-indexing system with cards is to assign a card to 



each item and then to record the terms applicable to the 
item on the card. Second thought, however, may lead to 
the opposite procedure: assign a card to each term, and 
record on each card the names or codes of all the items 
to which the term applies. A file organized the second 
way is an “inverse” file. Its main advantage is that, since 
ordinarily there are more items than terms, it requires 
fewer cards. In the Peek-A-Boo system, each card is di- 
vided into many small areas, one for each actual or antici- 
pated item, and an item is associated with a term by 
punching out the item’s area, thus leaving a hole, in the 
card for the term. When the desired term cards are piled, 
one on top of another, to form a deck, and are held up 
to the light, one sees light through the entire deck at the 
location for each item to which all the terms apply. This 
is, of course, merely one of several convenient imple- 
mentations of the logical “and.” It illustrates the natural 
congruence that exists between punched cards and Bool- 
ean algebra. 

Hybrid systems 

Because knowledge has a more complex structure than 
coordinate indexing can mirror, and still is less perfectly 
hierarchical than systems based on rootlike branching and 
exclusive categories must postulate, there have been sev- 
eral efforts to develop hybrid systems that would combine 
the advantages and avoid the disadvantages of hierarchi- 
cal and coordinate indexing. One such approach is to 
employ only a very few echelons of hierarchy and to use 
coordinate indexing within each echelon. Another is to 
build a quasi-syntactic structure upon the coordinate- 
index base by assigning role indicators to the terms. One 
may distinguish between Ri, wax made by bees, and Ro, 



bees made of wax, for example, by establishing an ad hoc 
two-echelon hierarchy: (a) product, (b) source or con- 
stituent. In that case we have: 

R: = (a) wax, (b) bees 
Rez = (a) bees, (b) wax 

Alternatively, one can define the role indicators: P = 
product, S = source, C = constituent. In that case we 

Ra = wax: (P), bees (CS) 
R2 = wax (C), bees (P) 

It seems unlikely, however, that such circumventions 
will lead to highly sophisticated or truly elegant storage 
and retrieval systems. The fundamental trouble seems to 
be that elementary set notation and Boolean algebra are 
inadequate to express compactly the subtle distinctions 
and intricate relations involved in a sophisticated repre- 
sentation and organization of the body of knowledge. In 
saying that, however, one should be sure to acknowledge 
that storage and retrieval systems based on sets and sub- 
sets have a particularly strong congruence with present- 
day information-processing technology and that, despite 
their limitations in sophisticated applications, they seem 
to be capable of achieving a high level of effectiveness in 
document retrieval and even in the retrieval of relevant 
passages within documents. That is to say, their short- 
comings seem likely not to manifest themselves strongly 
until an effort is made to deduce or infer consequences 
from the stored representation of knowledge. 




The basic notion of topological space analogy is that 
one item (document, fact, or idea) is the “neighbor” of 
another item to which it is closely related. Metric space 
analogy involves the notion of distance in addition to the 
notion of neighborhood: two items may be close to- 
gether or far apart, and the distance between them may 
be analyzed into n components corresponding to the n 
dimensions of the space. 

Metric space analogy is to some extent implicit in the 
many information-retrieval studies that have used prod- 
uct-moment correlation, multifactor analysis, and related 
“linear” methods. However, those studies have not em- 
phasized the space concept, and they have led to little or 
no consensus even about the dimensionality, much less 
about the identities of the dimensions, of any such thing 
as “information space” or “semantic space” or “the space 
of knowledge.” 

Doubtless the most literal application of the space 
concept has been Osgood’s “semantic differential,” based 
on factor analysis of many human scaling judgments re- 
lating linguistic “objects” (such as statements) to named 
attributive scales (Osgood, Suci, and Tannenbaum, 
1957). Osgood and his colleagues have shown, for ex- 
ample, that the same half-dozen basic factors appear in 
almost all human judgments, and that the fundamental 
affective dimensions are almost the same the world over 
— in 16 different ethnolinguistic contexts.* One can see 
a possibility of relating Osgood’s kind of semantic space 
to the space in which Swanson (1959) has determined 
correlations among the occurrences of descriptive terms 

* C. E. Osgood, Personal communication, March 1963. 



and to the space in which Giuliano has determined cor- 
relations of the contexts in which descriptive terms oc- 
cur (Giuliano and Jones, 1963; Giuliano, 1963). How- 
ever, that possibility has not been developed. There has 
been much use of methods that assume linearity (and in 
some instances statistical independence) of the basic 
variables, but not much explicit discussion of geometric 
space as a milieu for the representation of knowledge or 
intellectual processes. 

Much of our knowledge deals with the physical world, 
however, and must be indexed to the physical dimensions 
of space and time. Place names are in a sense merely 
spatial coordinates, and linguistic tense has its roots in 
physical time. It seems difficult, therefore, to conceive of 
a representation of knowledge within which a geometric 
framework does not play a major role. How can the at- 
tractiveness of the space analogy be reconciled with the 
obvious merits of logical and linguistic schemata that 
involve neither geometry nor continuous variables? 

The most promising approach, it seems to us, is to 
accept the notion that, for many years at least, we shall 
not achieve a complete integration of knowledge, that we 
shall have to content ourselves with diverse partial models 
of the universe. It may not be elegant to base some of the 
models in geometry, some in logic, and others in natural 
language, but that may be the most practicable solution. 


Williams, Barnes, and Kuipers (1962) have described 
an approach to document retrieval based on analysis of 
natural-language expressions (such as titles) in terms of 



arguments and functions. In our study, a similar approach 
was developed in terms of relations. 

Functions and relations appear to cover very nearly 
the same ground. If z is a function F of x and y, which 
we may write z—F (x,y), then there is a relation R 
among x, y, and z, and we may write it R(x, y,z). A 
relation may have any number of arguments. If it has one 
argument, it is merely an attribute or property. It seems 
reasonable to say that a single relation, say, Rio(a, b,c, 
d,- --,t), might subsume all the interactions described 
in a long sentence. 

The following discussion involves a long example based 
on a sentence of medium length. The example leads to 
the statement of a problem and an expression of belief, 
but not to a solution or a method. 

Earlier in this report there appears the sentence: “They 
will comprise the echelons of the memory hierarchy we 
have mentioned.” If a procognitive system were to try 
to make sense of that sentence, it would first have to de- 
termine the referents of the pronouns. Let us suppose that 
it is able (with or without human help) to figure out 
that “they” refers to computer memories that embody 
various compromises between small-and-fast and large- 
and-slow, and that “we” refers to the participants in the 
study, or perhaps to the author together with the readers. 
The sentence then amounts to the assertion that a com- 
plex relation exists among several entities: (a) computer, 
(b) the memories, (c) time, (d) the echelons, (e) mem- 
ory, (f) the hierarchy, (g) the participants, (h) com- 
prising, and (i) mentioning. 

To take up the matter a small part at a time, in an 
intuitively guided sequence, we may note first that there 



is a component relation that is nothing more than a 
simple qualification (of the common name) of a part of 
something by associating with it (the common name of) 
the thing of which it is a part. Thus we have “the com- 
puter memories,” a relation between (a) and (b) that we 
may represent as Rii(a, 6). In “the memory hierarchy,” 
we have another qualification, but of a slightly different 
kind. The hierarchy is not part of the memory. Instead, 
the memory is part of the hierarchy, or at any rate mem- 
ory is the stuff with which the hierarchical structure is 
filled. We may write Rizi(f, e), using the common initial 
subscript to signify the similarity, the distinct second sub- 
script to signify the difference, and the third subscript as 
a hedge against future complications. 

Next let us look at “the echelons” and “the hierarchy.” 
The echelons are abstract parts of the hierarchy. That re- 
lation we may call Rise(f, d), indicating that it is similar 
to, and also that it is different from, R12: (f, e). 

We put the verbs at the end of the list because verbs 
seem so much like operators and so different from sub- 
stantives. Nevertheless, let us represent the relation be- 
tween “time” and “comprising” as R3(c,h), and the 
relation between “time” and “mentioning” as Rs (c, 7). 

We come now to larger parts of the over-all relation 
of the sentence. Let us consider “the authors have men- 
tioned,” and then let us consider “the memory hierarchy 
the authors have mentioned.” The smaller segment is 
Rslg, Ra(c,i)]. The larger, which must include some 
equivalent to the notion that the memory hierarchy is in 
the main clause whereas the rest is in a dependent clause, 
is Ro{ Riarlf, e), R;[g, Rate; i)]}. 

The complex just constructed must mesh with “the 



echelons of the memory hierarchy.” The two ideas have to 
fit together in such a way as to indicate that we are plan- 
ning to move the train of thought forward with the eche- 
lons, but that we want to acknowledge that they are parts 
of a hierarchy, that the nature of the stuff of which the 
hierarchy is made is memory, and so forth. We may call 
the abstract meshing relation, together with its nuances, 
R;(x, y), and we may make it specific to the sentence in 
question by substituting the arguments: Ri(Riz2(f, d), 
Ro{ Riailf, e), R;[g, Rae: i)]}). 

Finally, to wind up the example with one big step, we 
may express the relation among “the computer memo- 
ries,” “comprising” (in the future), and all the rest that 
we have dealt with as: Rs [Ru(a, b), Rs(c, h), Ri (Rize 
(f, d), Re{Rioi(f, e), Rslg, Rs(c, i)]})]. That formula 
reflects to some extent the order in which the parts were 
combined. Since certain other sequences of partial com- 
bination would have been equally defensible, it is clear 
that there are alternative formulas that are equivalent to 
the one developed. 

Now, although the foregoing discussion is ridiculous 
in several ways, it is instructive in one way and challeng- 
ing in another. It is ridiculous in that the relational ex- 
pressions generated are complex and inscrutable, whereas 
the sentence itself was fairly simple and fairly clear. 
Unfortunately, it is probably ridiculous also in that we 
were able to set forth neither a taxonomy of relations nor 
an algorithm for simplifying the relational expressions. 
Nevertheless, the relational notation impresses us as much 
closer to an organizable, processible cognitive structure 
than is the sentence made of words. Note how much 
detail there turns out to be in a 12-word sentence. The 



relational notation makes the detail explicit and manipu- 
lable. It challenges us to develop taxonomies and simpli- 
fication procedures. 

Chomsky’s (1957) concept of transformational gram- 
mars offers a promising approach to both tasks. If Chom- 
sky’s methods were fully developed, one could transform 
any grammatical sentence into canonical form. Some of 
the information of the original sentence would then reside 
in a designation of the transformation that was applied, 
and some would reside in the canonical expression that 
resulted. On the average, of course, the canonical expres- 
sion would be simpler and more straightforward than 
the original. 

Even with some of the structural linguistic information 
factored out, much would remain in the canonical sen- 
tence. Some of that information would reside in the syn- 
tactic structure; more of it would reside on the nonsyntac- 
tic facets of the relations among the elements. Indeed 
there appear to be at least some thousands of significantly 
different relations among things. It is not entirely clear 
that the number is finite, even if we make an engineering 
interpretation of “significantly different.” However, it 
seems somewhere between conceivable and likely that 
the myriad and diverse observed relations are com- 
pounded of a few dozen — perhaps a hundred — atomic 
relations, and that the great variety arises when the atoms 
are combined in ways reminiscent of organic chemistry. 
_If it should turn out to be so, then relational analysis 
will almost surely be a powerful technique for use in the 
representation and organization of knowledge. 

Our initial thoughts along the line of relational analy- 
sis were concerned with relational nets, an example of 
which is shown in Fig. 4. 






Fig. 4. The diagram represents a relational network. The circles 
represent entities, relations, and properties. Entities, relations, and 
properties may participate in relations and have properties. The ulti- 
mate property is being a property. The “input terminals” of relations 
are marked by black spots. Ordered terminals are identified by num- 
bers. Interpretations of the diagram, such as “John and Jim are 
brothers, and John is taller than Jim” and “Jack has red hair,” are 
explained in the text. We hypothesize that, in a fully developed rele- 
vance net of this kind, the alphabetic labels can be erased without 
loss of any basic knowledge of the situation represented —1.e., with- 
out loss of any information other than that rooted in arbitrary selec- 
tion of unessential symbols. 

Consider the circles labeled “John” and “Jim.” That 
they represent individuals is indicated by their connection 
by arrows to “individual.” Individualism — being an in- 
dividual — is a property, as is shown by the connection 
from “individual” to “property.” Being a property is also 



a “property,” as is shown by the recursive arrow. John 
and Jim are both male, and they are siblings, therefore 
brothers. “Sibling” is an n-argument relation, and being 
an n-argument relation is a property. Every path ends at 

There are two “sibling” circles, for one set of siblings 
uses up a circle, and Jack and Jill are siblings too. As is 
shown by the arrows from the two “siblings” to “same,” 
being a sibling is the same as being a sibling. “Same” is 
an unordered, n-argument relation, and “unorderedness” 
and “n-argumentness” are properties. 

“Jack” and his “hair” participate in a whole-part rela- 
tion and also in a possessor-possessed relation. Both re- 
lations have two ordered arguments. The hair is red. 
Red is a color. Red and color are both properties. Thus 
Jack has red hair. It is all extremely simple at each step, 
but there seems to be room for many steps. 

As the number of relations and properties grows, the 
lower-level labels that are not wholly arbitrary can be 
figured out more and more readily from the higher level 
labels. In a complete relational net, all the unarbitrary 
information resides in the structure; the labels (other 
than the numbers that order the arguments of ordered- 
argument relations) are entirely superfluous. Relational 
nets are consequently very attractive as schemata for 
computer processing. 

During the last few months of the library study, Marill 
(1963) developed the idea of relational nets in the direc- 
tion of a predicate calculus (see Part II). Marill and 
Raphael are now simulating net structures on a computer 
and developing programs that will organize and simplify 




Much of the research during the second year of the 
study was devoted to question-answering systems. The 
system constructed by Black, to be described briefly in 
Part II, was based on the representation of information in 
the form of statements in first-order predicate calculus. 
With the information in that form, and with the aid of 
computer programs designed to process it, the computer 
could deduce from its information base answers to vari- 
Ous questions stated in the formalism (Black, 1963). We 
believe this to be a significant development. It demon- 
strates the advantage of employing a formalism that ap- 
proaches the sophistication and complexity necessary to 
represent efficiently the subtleties and intricacies of 
thought and knowledge. 

Two other researches are using predicate calculi in 
ways somewhat similar to Black’s. Bohnert,* of the 
Thomas J. Watson Research Center of I.B.M., is using 
the first-order predicate calculus. McCarthy,+ of Stan- 
ford University, is using a second-order predicate calculus 
that degenerates to first order when time is held constant. 

Two severe practical problems are encountered along 
the path taken by predicate calculus. First, there is as yet 
no way to translate automatically from statements in 
natural language to statements in predicate calculus; the 
translation must be made by people, few people can do 
it, and the process is time consuming. Second, a small 
amount of natural language turns into a large amount of 
predicate calculus. The first problem is, of course, a basic 

* H. G. Bohnert, Personal communication, November 1963. 
+ J. McCarthy, Personal communication, November 1963. 



research problem as well as a practical one. The second 
problem places its demand upon the information tech- 


At this stage it seems to be a very good hypothesis that 
languages of high order are required for compact repre- 
sentation of knowledge, and it seems to be a fairly good 
hypothesis that such languages are required for efficient 
processing of knowledge. Even highly complex things 
can be said in very simple languages. For instance, if 
there were an element in a set for every possible state- 
ment, one could make any statement merely by pointing 
to its element. However, in low-order languages (such 
as the language of elements, sets, and Boolean operators) 
the representation of a complex molecule of knowledge 
is disproportionately voluminous. Our perception of this 
matter, though still somewhat nebulous, has led us to a 
rather firm conviction: that the economic and practical 
advantages of linguistic sophistication are great, and that 
the intellectual advantage is even greater. 

The conviction just set forth is coupled with a second 
conviction that is less firmly set but is nevertheless a work- 
ing conviction: in a language to be used in procogni- 
tive systems, formality is an extremely valuable asset. 
Both the lack of formality and the failure to adhere 
strictly to the rules can cause great difficulties in all kinds 
‘of machine processing of information. The problem is 
not the inconvenience caused by grammatical errors or 
ambiguities of vocabulary, but rather the high price that 
civilization pays for the capability that lets man navi- 
gate through his sea of syntactic sorrow and semantic 



confusion. It is almost obvious that man’s inability to 
organize the corpus of his knowledge tightly is due to his 
having to squander such a wealth of intellectual resource 
each time he reads a paragraph. For all these reasons we 
strongly favor the idea of developing high-order formal 
languages and applying them with machine assistance in 
organizing the body of knowledge. 

Natural English is a high-order language, of course, 
and, when correctly written or spoken, it may even ad- 
here to a definite form — though surely no one knows 
quite what the form is. If we try to say what is wrong 
with English, perhaps we can sharpen the concept toward 
which we are pointing. 

The main shortcoming of English, and presumably of 
any natural language, is its ambiguity. Natural languages 
are so often used as adjuncts to nonlinguistic processes 
that natural languages do not have sufficient chance to 
practice independence and to develop self-sufficiency. 
Moreover, when they are exercised in isolation from non- 
linguistic processes — in reasoning out solutions to diffi- 
cult problems, for instance — there is very little op- 
portunity to track down sources of error or confusion. 
Thus ambiguity persists because it creates no serious 
difficulty in situations in which the difficulty could be 
detected and corrected, and ambiguity is rarely detected 
in situations in which it creates great difficulty. It is no 
wonder, therefore, that “in” and “of” stand for twenty 
different relations each, and that “When locked enter 
through 3D-—100” does not tell you what to do when you 
are locked, nor does it tell you to go through room 
3D-100 when the door of 3D—100 is locked. 

In short, the trouble with English as a carrier of knowl- 
edge is the horrendous amount of calculating on a very 



large base of data that is expended just to decide which 
of several locally plausible interpretations of a simple 
statement is correct or was intended. If the greater part 
of man’s capability is wasted in that kind of processing, 
he does not have enough left to achieve more significant 
goals. This conclusion is obvious when the processing of 
English text is attempted by a present-day computer. It 
is less obvious but probably just as true for people. 

The higher-order language that we propose as an effec- 
tive carrier for knowledge is a kind of unambiguous 
English. As long as changes of context are signaled 
explicitly within the language, no serious problem is in- 
troduced by dependence on context. (Indeed, depend- 
ence on context appears to be necessary for the achieve- 
ment of efficiency in diverse special applications.) The 
proposed language would recognize most or all of the 
operations, modulations, and qualifications that are avail- 
able in English. However, it would quantize the continu- 
ous variables and associate one term or structure un- 
ambiguously with each degree. Finally, the system in 
which the proposed language is to be implemented would 
enforce consistent use of names for substantives; it would 
monitor “collisions” among terms, ask authors for clarifi- 
cations, and disallow new or conflicting uses of estab- 
lished symbols. 

All this advocacy of unambiguous, high-order lJan- 
guage may encounter the disdainful accusation, “You're 
just asking for ruly English!” However, the situation is 
more favorable now for a ruly version of English than it 
ever has been, and it will be fully ripe before the new 
language is likely to be developed. The situation will be 
ripe, not because people will be ready to adopt a new 
dialect, but because computers with large data bases will 



need the new dialect as an information-input language. 
The envisioned sequence is: from (1) the natural (tech- 
nical) language of the journal article through a machine- 
aided editorial translation into (2) unambiguous English, 
and then through a purely machine transformation into 
(3) the language(s) of the computer or of the data base 
itself. At any rate, this is a plausible approach that de- 
serves investigation, though the areas discussed earlier, 
particularly relational nets and higher-order predicate 
calculi, will surely provide competitive approaches. 



Man-Computer Interaction 
in Procognitive Systems 

IN THE FOREGOING CHAPTERS, the concept of the procog- 
nitive system has been approached and developed from 
several different points of view. Common to these points 
of view has been the fundamental purpose, to improve 
the usefulness and to promote the use of the body of 
knowledge. Also common to the several points of view 
has been the central methodological theme, that the pur- 
pose can best be achieved through intimate interaction 
among men, computers, and the body of knowledge. 
- Though we shall use the convenient phrase, “man-com- 
puter interaction,” it should be kept in mind that it is an 
abbreviation and that the body of knowledge is a co- 
ordinate partner of the men and the computers. 

In order to come to grips with problems of the pro- 
jected interaction, it seems necessary to break it down 



into parts, even though there appears to be no set of 
components into which the interaction process can be 
subdivided that do not themselves interact strongly. 

The traditional approach, which allocates some func- 
tions to men and others to machines, is particularly un- 
satisfactory because, in order for the major functions in- 
volved in working with the body of knowledge to be ful- 
filled efficiently, synergic action is required in which men 
and machines participate together. Most of the efforts 
made during the last decade to figure out “what men 
should do” and “what machines should do” have missed 
this point widely. They have supposed that the fabric of 
man-computer interaction is a patchwork quilt made of 
red and blue patches, and that the red patches correspond 
to functions that call only for human capabilities, the 
blue patches to functions that call only for machine capa- 
bilities. In our analysis, however, the fabric of man-com- 
puter interaction is an almost uniformly purple quilt, 
albeit made of red and blue threads. Woven together, 
they constitute a useful whole. But when one tries to di- 
vide it into human functions and machine functions, he 
winds up not with two sets of assignable tasks but with 
two tangles of colored thread. By and large, the human 
threads are heuristic and the machine threads are algo- 
rithmic. The art of man-computer system design is the art 
of weaving the two qualities into solid-color cloth. 

Despite the artificiality of division, we shall divide the 
discussion of man-computer interaction, for convenience, 
into three parts: (1) what is often called the man-ma- 
chine interface, the physical medium through which the 
interactions take place, (2) the language aspects of man- 
computer interaction, and (3) a look at the total process 
as an adaptive, self-organizing process. These three sec- 



tions re-examine some ideas already introduced and fit 
some new elements into the picture. 


Early in our study of man-computer interaction, we 
became dissatisfied with the term, “man-machine inter- 
face.” “Interface,” with its connotation of a mere surface, 
a plane of separation between the man and the machine, 
focuses attention on the caps of the typewriter keys, the 
screen of the cathode-ray tube, and the console lights that 
wink and flicker, but not on the human operator’s 
repertory of skilled reactions and not on the input-output 
programs of the computer. The crucial regions for re- 
search and development seem to lie on both sides of the 
literal interface. In order to remind ourselves continually 
that our concern permeates the whole medium of inter- 
action, we have avoided “interface” and have used, in- 
stead, “intermedium.” 

The man-computer intermedium subsumes the com- 
puter’s displays and the mechanisms and programs that 
control and maintain them, the arrangements through 
which people communicate information to the computer, 
and the relevant communication organs and skills of the 
men. Once we assume that definition of the domain, it is 
impossible to draw a sharp line between the nonlinguistic 
and the linguistic parts. The blurred line that we shall 
in fact draw is intended to put most of the questions of 
- apparatus on one side and to put most of the questions 
of method, procedure, and format on the other. 

An important part of the physical intermedium is the 
user’s station, a “console” of the kind described in an 



earlier chapter. However, the intermedium extends be- 
yond the console to include the user’s entire work space 
and the physical aspects of his personal documentation 
system and perhaps even his laboratory system or his ap- 
plication system. We shall not examine those extensions 
in detail, but that does not mean that we consider them 

The Oscilloscope-and-Light-Pen Schema 

According to the argument set forth in the introduc- 
tion, it is natural to think in terms of familiar schemata, 
but it is necessary to abstract from them, or to break them 
down and recombine them into new configurations, al- 
ways on the lookout for new elements, if one is to progress. 
One of the two most flexible and promising display- 
control systems provided by the current technology is the 
combination of oscilloscope and light pen that has figured 
in much of our discussion. That combination is a source 
of very useful schemata. It is also a source of intense 

Abstracting from the actual physical equipment, and 
making several improvements and rearrangements in the 
mind’s eye, one comes to a conception that may well be 
epoch-making as soon as it is well engineered for man’s 
use and widely available. We make this prediction despite 
the fact that the currently available and familiar equip- 
ment has such shortcomings that pencil and paper and 
the printed page seem to belong to a domain of infinitely 
superior engineering. 

The desiderata are easy to list but probably difficult to 
achieve. In the following list each item is associated with 
an intuitive estimate of its degree of importance. The 



estimate — the number in parentheses — is based on a 
scale of increasing importance from 0 to 10. 

\/ We should like to have: a color display (4) if possi- 
ble, or, if not, a black-on-white display (7) with at least 
eight gradations of brightness (5) and a resolution ex- 
ceeding 400 (4), or 200 (6) or, at any rate, 100 (9) 
lines per inch. Each element of the display should be 
selectively erasable by the computer program, and also 
either directly or indirectly by the operator (9). The dis- 
play should have controllable persistence (6) and should 
be free of flicker (9). There should be a way to capture 
any frame of the display in the form of “hard copy” (9), 
and the hard copy should be coded automatically for 
machine filing, retrieval, and redisplay (7). 

The display should provide the set of features called 
“Sketchpad” features (10), which assign to the computer 
those parts of the sketching and drawing skill that involve 
much practice and precision, and leave the man responsi- 
ble mainly for expressing the essential structure of the 
concept he desires to represent. 

The stylus should resemble an ordinary pen or pencil 
in size, shape, weight, and “feel” (8). It should have a 
home position slightly above and to the right of the dis- 
play surface. It should return to that resting place when- 
ever the operator releases it from his grasp. If the stylus 
is connected by a wire to the console, the wire should be 
very light and flexible and should not constrain the 
manipulation of the stylus. 

In addition to the foregoing considerations, there are, 
of course, reliability (9), ruggedness (8) and economic 
feasibility (10). The challenge inherent in the last three 
factors sometimes seems to be too little appreciated. 

The oscilloscope-and-light-pen schema of the next 



decade should have a hard, tough surface upon which 
both the user and the computer can print, write, and 
draw, and through which the user’s markings will be com- 
municated to the computer. Even when this surface is 
flush with the top of a desk, no “electron gun” sticks 
down through the desk and bumps the user’s knees. The 
marks appear on the surface, of course, and not on a 
lower subsurface: there is no explosion screen and no 

Ideally, the user and the computer should make their 
marks in precisely the same coordinate frame, so that it 
will not be necessary to compensate for poor registration. 
It is easy and natural to designate part of an observed 
pattern by pointing to it or touching it directly with 
fingertip or stylus. Since the computer must act upon 
designations made by the pointing or touching of pat- 
terns displayed on the screen, it seems to us important to 
have the frame of reference for sensing correspond pre- 
cisely to the frame of reference for displaying. It may 
be easier to develop equipment in which the user and 
the computer make their marks on separate screens, but 
whether that is a satisfactory arrangement should be 
evaluated carefully. 

A device called the RAND Tablet* has been developed 
which will provide experience with separate display sur- 
faces for man and computer. The RAND Tablet looks 
to the user like a sheet of paper. Underneath the paper, 
however, there is a layer of insulating material on each 
surface of which are a thousand or more parallel con- 
ductive lines. The lines run from right to left on one 

* The RAND Tablet is similar in principle to a device invented by 
H. M. Teager of M.I.T. Both devices involve conductive lines and 
coded pulses. The path from the lines to the stylus is capacitative in 
the RAND Tablet and inductive in the Teager Table. 



surface of the insulating sheet and from front to back on 
the other. Coded patterns of pulses are applied to the 
various conductive lines by a pulse generator. When the 
user touches his stylus to the paper, the stylus picks up 
the pulses from the nearest conductive lines and transmits 
them, by way of a connecting lead, to electronic circuits 
associated with the pulse generator. These circuits de- 
termine the location of the tip of the stylus and transmit 
the coordinates to the computer. Because the conductive 
lines are produced in the same way as printed circuits, 
the Tablet is not very expensive. Doubtless, the electronic 
circuits that generate the coded pulses and determine the 
location of the stylus can eventually be produced at low 
cost as “integrated circuits.” 

The Tablet handles only the problem of communica- 
tion from the user to the computer and does not provide 
a display from computer to operator. At the RAND 
Corporation, the Tablet is used in association with a 
computer-posted oscilloscope display. The Tablet is 
mounted flat on the writing surface of the console. The 
screen of the oscilloscope is vertical and located immedi- 
ately behind the Tablet. On the basis of early experience, 
the RAND people say that the separation of the com- 
puter’s display from the user’s Tablet is not a source of 
serious difficulty. 

A working version of another component of the 
schema we have been discussing is provided by the “flat” 
cathode-ray tube. Whereas an ordinary cathode-ray tube 
has the (often inconvenient) shape of an Erlenmeyer 
flask, the flat tube has the shape of a book. Operable flat 
oscilloscopes have been constructed and have proved to 
afford excellent resolution. 

In several military display systems, projection of pho- 




tographic “slides” is combined with display generation by 
computer. The slides provide a convenient and economi- 
cal way of maintaining the static part of the display pat- 
tern, which is often a reference grid or a map. A few 
of the systems provide means for photographing com- 
puter-posted displays and then redisplaying the informa- 
tion from slides. We think that, should such an arrange- 
ment be produced at low cost, it would find widespread 
use (7) in procognitive systems. 

The typewriter schema 

The second main area in which improvement of con- 
trols and displays is required is the area of alphanumeric 
keyboards and hard-copy displays. Those devices are 
obviously important for the future of procognitive sys- 

The main functions to be fulfilled by devices derived 
from the typewriter schema are obvious ones. Such de- 
vices must provide for “digital” communication from the 
user to the computer. They must also provide a visible 
record of the information that has been fed into the com- 
puter, and this record should be easy to modify. The 
computer should be able to make marks on the record. 
The user should have the option of producing either soft 
(ephemeral) copy or hard (permanent) copy, and even 
the option of turning the soft copy into hard copy after 
editing. The device should accept input as fast as a well- 
trained operator can provide it, and it should translate 
signals from the computer into typed characters at a rate 
of at least 100 characters per second. 

The typewriterlike devices that are currently available 
provide five schemata that are useful as bases for think- 
ing in this area. Let us abstract from these schemata the 



parts and qualities we should like to have melded to- 
gether for use in procognitive systems. 

_/The first schema is offered by the familiar teletype- 
writer. Although there are several models of teletype- 
writer, the general features are sufficiently characteristic 
that, for some of our purposes, we may think simply of 
“the” teletypewriter. Compared to most other man-com- 
puter communication devices, it is rugged, reliable, and 
inexpensive. However, it has no lower-case letters, it is 
slow, and it has a strange “touch” for anyone accustomed 
to office typewriters. Many people who have had experi- 
ence with on-line man-computer interaction look for- 
ward to the manufacture of a device like current models, 
but with 128 or more characters, with the capability of 
typing at very high speed, and with a touch more like that 
of an ordinary electric typewriter. We realize, however, 
that, in the present state of the art, these features are to a 
large extent incompatible with high reliability and low 

The schema offered by the familiar electric typewriter, 
or by the electric typewriter that has been designed or 
adapted to be used with a computer, is a montage of 44 
type keys, six to twelve operation keys, two cases, fairly 
clear marks on paper in one or two colors, limited con- 
trol of the carriage from the keyboard, fair reliability, and 
a high level of noise. The ensemble of 88 characters 
(2 cases X 44 keys) is almost large enough for serious 
intellectual purposes. The font contains both capital and 

lower-case letters. These things are important. Perhaps 

even more important are the intimate familiarity with 
typewriters and the significant skill in typing that are 
fairly widespread in the population. 

It is worth pausing to ponder how few well-developed 



skills there are that are both complex and widespread. 
Almost everyone can get about in three-dimensional 
space. Almost everyone can speak and understand one 
of the natural languages — perhaps not grammatically, 
but fluently. But relatively few people can do anything 
else that is even remotely comparable in informational 
complexity and degree of perfection. Of the remaining 
candidates for inclusion in the list of widespread complex 
skills, we may with some misgivings accept writing, and 
perhaps the playing of musical instruments. After this 
comes typing. And typing ends the list. It is possible 
that, in future decades, typing will move up past music 
and that it will become almost as widespread as writing 
and more highly developed. 

The third typewriter schema is offered by typewriter- 
like devices that are used in association with computers 
and that type 60 or more characters per second. These 
are usually called “printers.” (We refer here to character 
printers and not to line printers, which are likely to re- 
main too complex and expensive for ordinary user sta- 
tions.) The aspect of the printer schema that is of inter- 
est is simply the rapid rate of typing. It is worth while to 
have in mind that characters can be marked on paper 
at high speed by a device the size of a typewriter. 

The fourth schema is offered by devices commercially 
available but not in widespread use, separated keyboards 
and typing units. When a typewriter is associated with 
a computer, there ceases to be any reason for the con- 
ventional, direct connection between the key that is 
pressed and the type bar that strikes the paper. Obviously 
the pressing of the key should direct a code to the com- 
puter, and the computer should acknowledge the code by 
activating the type bar and thus printing the character on 



the paper. As soon as the tacit assumption of a direct 
linkage between key and type bar is recognized and dis- 
carded, there is no longer any need to maintain a one- 
to-one relation between key pressings and character mark- 
ings. It should be possible, in a “debreviation” mode, to 
type “clr” on the keyboard and have “The Council on 
Library Resources, Inc.” appear on the display. 

The final schema in the typewriter field is the “Steno- 
type.” The component that seems to be of most value, 
for these purposes, is simultaneous multiple key pressing. 
If it turns out, as seems likely, that very large ensembles 
of characters are desirable in man-computer interaction 
with the body of knowledge, then it will become much 
more important than it is now to be able to specify the 
desired character by pressing a pattern of keys on a small 
keyboard. That is a much better solution than pressing a 
single key on a keyboard with several thousand keys. 

Displays for group-computer interaction 

Because our thinking is anchored in familiar experi- 
ences, we are inclined to think of interaction between 
men and procognitive systems as a collection of dyadic 
man-computer interactions. More and more, however, 
the problems of science, technology, industry, and gov- 
ernment are being solved by groups of men rather than 
by individuals. Although the “team approach” is a topic 
of controversy in some fields, its value has been proven 
in others. Consideration should be given to the develop- 
ment of tools and techniques to facilitate group interac- 
tion with the body of knowledge. 

In the current technology there are two general ap- 
proaches to group-computer interaction. The first and 
most widely used provides a separate console for each 



member of the group and relies upon the computer, to- 
gether with auxiliary communication circuits, to mediate 
the interaction among the members of the group as well 
as the interaction of the members with the computer and 
its store. The second approach, taken in some military 
systems, uses large “wall” displays, located in view of 
several or all of the members of the group and intended 
to provide a common frame of reference for their deci- 
sions and actions. 

In procognitive systems based on individual consoles, 
the main items of equipment that would be needed for 
group communication, not already discussed under the 
heading of man-computer communication, would be de- 
rivatives of the telephone and television. Communication 
by telephone and perhaps by television would be closely 
correlated with communication through the computer 
system. This procedure does not pose any novel require- 
ments for the display and control equipment of the sys- 

In a procognitive system with group displays (second 
approach) one would expect to see large-scale displays 
similar in principle to the individual display screen al- 
ready discussed, even with derivatives of the light pen 
to provide communication between the human members 
of the team and the computer. The most significant char- 
acteristic of the group display seems to us to be resolu- 
tion. The total number of resolvable points is no greater 
in the large-scale kinematic displays available at present 
than it is in the smaller individual displays. In some large- 
scale static displays, such as wall maps, however, there is 
high resolution, and in them the advantage of size is 
apparent. On a good wall map, one can see the general 
features of a continent from the middle of the room. In 



order to examine the boundaries of countries or states, 
it is necessary only to step a little closer. From a normal 
reading distance, the names of cities and towns can be 
made out and the courses of rivers can be followed. It 
is interesting to extrapolate to very high resolution and 
dynamic presentation. If display capabilities should in- 
crease as rapidly as memory capabilities, one may some- 
day watch a display on a very large wall, examine the 
weather situation in the Midwest, and then with a mag- 
nifying glass follow the movement of an individual auto- 
mobile from Bethesda to the Pentagon, reading the names 
of the streets and highways along which it moves. It seems 
to us that there is some merit in trying to develop such 
large-scale, high-resolution dynamic displays for group- 
computer interaction, though at the same time we ap- 
preciate the difficulty of the technical problems involved. 

Consoles and work spaces 

The design of consoles and the arrangement of work 
spaces is not likely to be regarded as an exciting part of 
library planning, but it is an essential step in overcoming 
what C. W. Churchman calls the “brain-desk barrier.” 
During the course of our experience with facilities for 
man-computer interaction, the point was driven home to 
us that convenient arrangement of the elements of the 
physical intermedium is an extremely important factor 
in the determination of the effectiveness of the interaction 
and not something readily purchased or easily achieved. 
The individual ingredients of the current difficulty of 
man-computer interaction are trivial in themselves, but 
they add up to a significant total. 

The first inconvenience is likely to be the position of 



the keyboard of the computer typewriter. Most computer 
typewriters are located on “console” desks that are higher 
than typewriter tables. The keyboards of most computer 
typewriters stand higher above their resting surfaces than 
the keyboards of ordinary typewriters do. That makes the 
keyboard much too high. Raising the chair seat makes 
the typist’s knees hit the table. Moving the computer 
typewriter to a conventional typewriter table puts the 
typewriter too far away from the oscilloscope screen, the 
light pen, and the switches that control the computer. 

In the computer-posted displays that we have seen, the 
oscilloscope screen stands only 10 or 20 degrees off 
the vertical. This is true for displays that have light pens 
associated with them as well as for screens that function 
only as displays. Evidently, the designers had in mind the 
blackboard and not the writing desk. In fact, one’s black- 
board habits carry over to the vertical oscilloscope 
screen, and one writes and draws in a large scale inap- 
propriate to the small size and high resolution of the dis- 
play. Moreover, it is tiring to hold a hand at eye height 
for a long time without support. 

The other elements of difficulty are of the same general 
nature: Light pens are too thick and heavy for facile 
writing; they could be and should be the size and weight 
of ordinary pens. Conventional “line printers” have no 
lower-case letters; it is difficult to read long passages in 
capital letters. One has to turn the lights out to read the 
oscilloscope and then turn them back on to read print or 
typescript. And so forth. Each individual difficulty can 
be remedied easily, but it may take a strong, well-organ- 
ized effort to perfect all the necessary elements and com- 
bine them into an effective physical intermedium. 




If the problems of the physical intermedium of man- 
computer interaction are lacking in intellectual challenge, 
the problems of language for man-computer interaction 
abound in it. The entire spectrum of language from bi- 
nary machine code to the great natural languages will be 
involved in man’s interaction with procognitive systems. 

We may distinguish four different involvements of 
language in a neolibrary procognitive system. Language 
is employed (1) by the programmers who prepare and 
improve the computer programs that implement the 
basic operations of the system; (2) by the information 
specialists who endeavor continually to improve the or- 
ganization and operation of the system; (3) by the sub- 
stantive users of the system in their interaction with the 
body of knowledge; and (4) in the representation of the 
body of knowledge in the memory of the system. 

The science of applied linguistics is so new, and formal 
languages designed to facilitate the programming of com- 
puters are burgeoning so fast, that it is difficult to sum- 
marize the present situation and almost impossible to 
make a long-term projection worthy of confidence. This 
part of the discussion, therefore, is confined to a brief 
examination of the roles of language in the four areas of 
man-computer interaction. 

Programming language 

Even after it has been developed and is in operation, 
the procognitive system of the year 1994 has a continuing 
need for programming specialists. Their task is, essen- 
tially, to maintain and improve the basic programs of the 
system. Requirements arise that cannot be met effectively 



without making alterations in the basic system programs. 
To change even one short statement in a large system of 
programs is a serious matter, to be undertaken only by 
the most skilled and experienced professional program- 
mers. The programmers of the procognitive system, 
therefore, plan modifications carefully, test them thor- 
oughly outside the main stream of operation of the system, 
and then monitor the situation closely when they intro- 
duce the modifications into actual operation. 

Basic programs for the procognitive system are writ- 
ten in a high-level programming language. The programs 
are available to anyone who wishes to examine or use 
them, not only as services that will function at the user’s 
request, but also in the form of annotated statements in 
the programming language. When a system programmer 
modifies a system program, he operates upon it through 
another program designed to facilitate the preparation, 
testing, modification, documentation, storage, and re- 
trieval of programs. This “programming-system” pro- 
gram provides several separate services, each of which 
can be brought into operation by simply typing its name. 
Each service makes available to the programmer a spe- 
cialized language attuned to its own structure and func- 
tions. Together with his physical intermedium, the pro- 
gramming languages guide and implement the program- 
mer’s interaction with the system. 

Let us observe a programmer at work, modifying a 
basic graphical display program in order to make it 
operate with a new display device that provides eight 
times as much linear resolution as the older “standard” 
displays provide. We shall concentrate more on the func- 
tions and operations than on the syntax and terminology. 

The programmer sits down at his console, turns it on, 



and calls the programming system. It reports for service, 
and he requests the program retrieval and editing pro- 
gram. It reports, and he asks it for the names of all the 
screen-display programs that are regularly used. Of the 
20 offered, he selects four as likely to include the one 
he must change, and he asks to see their abstracts. From 
the abstracts, he selects one and asks to see its listing in 
the high-level language in which it was written. From the 
listing, he can tell almost at once that he has the pro- 
gram he wants, for the listing is heavily annotated, and 
it is immediately clear that the number of lines in each 
scan is the familiar number and that the division of the 
screen into sectors is the same as the one currently in 
use throughout the system. 

Using the “moving window” * to scan through the 
program, the programmer gets an over-all picture of the 
display routine and then returns to its beginning, where 
he finds a statement labeled “Interrogate display” and a 
comment to the effect that, through interrogation, the 
program checks the type designation of the screen on 
which it will display information. Such a check is made 
because at one time or another several different types of 
display have been in the system. What is being introduced 
is a fifth or sixth type. The programmer figures out that 
the result of the interrogation is a code, that the first ten 
digits of the code are checked against a standard to pro- 
vide authentication, and that the last four digits of the 
code are used as the argument of a “transfer-table” or 
_“jump-table” operation. He therefore temporarily dis- 
misses the entire programming system, calls a document- 

* A “moving window” display that lets the programmer see a select- 
able and variable segment of program or data in the computer memory 
has been demonstrated by Marvin Minsky and associates at M.I.T. 



retrieval program, and retrieves the engineering specifica- 
tion of the new display device. He looks at the document 
long enough to satisfy himself that he has the correct 
display program. He then asks the question-answering 
system the identification code of the display described in 
the document. Just as the question-answering system is 
displaying the answer, he finds the passage in the docu- 
ment that gives the code. He makes sure that the two dis- 
plays of the code are identical. He sees that the last four 
bits are 1101 (decimal 13). He makes a note of that and 
recalls the programming system, which comes back into 
action with the retrieval and editing program operating in 
the mode in which he last used it. 

The programmer notes next that the name of the trans- 
fer table is “Display-selector transfer table.” He types: 

Insert in Display-selector transfer table 
+ 13 a jump to Patch. Prepare Patch: 
Deposit in user's scratch-pad register 
(Insert here designation of user's 
scratch—pad area used in the first deposit 
operation following the jump-—out from 
Display-selector transfer table + 12) the 
octal constant [Insert here (the octal 
constant inserted in last-cited deposit 

Then on the next line he types: 

Jump to the return used by Display- 

selector transfer table + le. 
Then finally he asks to see a screen display of the newly 
prepared patch program. In the first register of the patch, 
the programming system has composed the instruction 
“Deposit,” the octal constant “4000,” and the symbolic 
address “Scratch-pad area, Display + 6.” In the next 
register it has deposited the instruction “Jump” and the 



symbolic address “Initialize display.” Seeing this, our 
programmer thinks a moment about the “4000” and de- 
cides that it is a plausible value for the constant. He then 

Display "Initialize display". 
and looks at the part of the program to which the “Jump” 
leads. The program statements in that segment seem ap- 
propriate to him. He therefore decides to test the modified 

The modification has been so short and simple that 
he sees no reason to test it under “trace,” which would 
have the effect of running it slowly and protecting other 
programs against interference in the event that it should 
misbehave. However, it does occur to him to take one 
more precaution before testing. He decides to give the 
patch a retrievable label (to replace the local, nonretriev- 
able label, “Patch”) and to store away a copy of the 
modified program so that he can retrieve it and work on 
it further if the test is unsatisfactory. He therefore types: 

Assign the label, "Constant for display 

13" to the statement locally labeled 


The programming system pauses a moment and then re- 

The label, “Constant for display 13”, has already been used. 
It occurs to him immediately what the trouble is, and he 

Assign "Constant for display 15, H. I. 

The programming system then replies: 




In order to test the program, the programmer takes ad- 
vantage of a substitution technique provided by the test- 
ing part of the programming system. He calls for the 
testing service and then asks to have a set of test data 
displayed on his old display screen, which is still con- 
nected. As soon as he has confirmed that the data are 
displayed on his old display screen, he substitutes one of 
the new display devices for his old one and requests the 
test program to substitute the program he has just modi- 
fied for the regular program. The system will effect the 
substitution as soon as it comes to the part of the regu- 
lar program that matches (except for the modification) 
the part he has just modified. This is a fairly complicated 
operation, but it is easier and surer for the system to find 
the place where the substitution should be effected than 
it is for the programmer to specify it precisely. The pro- 
grammer then calls for a redisplay of the data. This time 
the data show up on his new display, the format is the 
same, the resolution is better, and everything seems to be 
working well. 

Now the programmer acts on the insight he had 
achieved a minute or two earlier — the realization that 
one or more than one other programmer has been work- 
ing on the modification, programming it in parallel with 
him, so that two or more versions of the modification can 
be compared before the modification is introduced into 
the operating system. He therefore asks the programming 

Is another programmer modifying a screen-— 
display program? 

The programming system says that no one is working on 
such a modification now, but that O. B. Smith worked 



on one earlier in the day. Our programmer asks to talk 
with O. B. Smith. The system makes the connection. The 
two programmers discuss their modifications on the tele- 
phone. Each calls for both versions of the modified pro- 
gram in order to check that they are identical in their 
essentials. It turns out that O. B. Smith had happened 
upon the label, “Constant for display 13,” only an hour 
or two earlier than H. I. Johnson and had thereby set up 
the collision of labels that had been detected by the sys- 

The two versions of the program do match, except in 
respect of arbitrary labels. The two programmers to- 
gether delete the retrievable patch label. They enter a 
note in the system log, indicating that they have made a 
modification and giving its location, purpose, and nature, 
and their names and the date. Then they replace the 
old program with the modified program in the system 
files and in the operating system. In that final step they 
use — without stopping to think about it — a sophisti- 
cated capability of the system. Before substituting the 
modified version for the old one, the system checked to 
see that no one was at the moment in the process of using 
the old one. 

In the foregoing account, attention was focused on 
the programming system and the language and procedure 
used in modifying a program — not on the programming 
language in which the system programs were originally 
written. There is little to say about the basic program- 
ming language except that it is a powerful and sophisti- 
cated descendent of present-day computer-programming 
languages and the product of long-continued, intensive 

The example was intended to illustrate how closely 



interwoven are the threads of hardware, system design, 
programming technique, and programming language. It 
touched also upon the capability of the programming 
system to interrelate the efforts of members of the sys- 
tem programming team. 

Organizing language 

The system specialists in their continual effort to im- 
prove the organization of the body of knowledge and the 
usefulness of the procognitive system must deal with a 
variety of languages and procedures. Whereas the lan- 
guage of the programming system dealt with programs 
written in a consistent, high-level programming language, 
the “organizing language” dealt with documents written 
in the various natural languages, with mathematical 
models and computer-program models, with computer 
programs themselves, and — most intensively — with 
large, coherent systemizations of knowledge represented 
in the “representation language” that constitutes the foun- 
dation of the question-answering system. Associated with 
each of these diverse objects is a set of information struc- 
tures and procedures with which the organizing lan- 
guage must resonate. Therefore, the organizing language 
is divided into parts, any one of which may be brought 
into play almost instantly by any one of the system 
specialists who can direct it on the task with which he is 

One of the main tasks of the system specialists is to 
transfer information from the document store associated 
with a subfield of knowledge to the organized body of 
knowledge of that subfield. This operation involves, but 
is not wholly restricted to, translation from natural lan- 
guage to the representation language. We say that it is 



not wholly restricted to such translation because many 
of the documents entering the procognitive system con- 
sist mainly of metainformation and are intended not to 
express substantive findings so much as to set such find- 
ings (represented in auxiliary structures) into relation 
with problems, to define their scopes and domains, and 
to offer qualifications and suggestions about their ap- 
plications and their correlation with the body of knowl- 
edge. The substantive findings are typically expressed in 
the form of a high-order formal language, or in the form 
of operable computer programs (“dynamic models”), or 
in the form of data structures such as lists, tables, and 
matrixes. The fact that the incoming information is al- 
ready to a large extent formalized and organized simpli- 
fies the problem for the system specialist. During the fu- 
ture period to which this discussion refers, many scien- 
tists are beginning to use in their own work the formal 
language in terms of which the body of knowledge is 
represented in the procognitive system. However, the 
rapid advance in the understanding of languages and the 
burgeoning of linguistic techniques and formalisms are 
still in progress, and the time when one great formal 
language will dominate procognitive intercourse appears 
still to be decades away. 

The organizing language contains a section that han- 
dles translation from natural language to the represen- 
tation language. This section provides translators for 
each of the main natural languages. These translators 
work in both directions. One of the major items of routine 
business is to translate a document into the formal repre- 
sentation language, then to translate it back into a long 
series of short statements in the natural source language, 
and then to review the new version in a conference with 



the author of the document. The system specialists who 
handle that part of the work are educated in the scientific 
or technical disciplines they deal with, as well as in the 
information sciences. 

The process of translation is carried out mainly by 
algorithm, but, even after two decades of progress, there 
are still many problems and difficulties that must be 
solved or circumvented by human specialists. The prob- 
lem of capturing and representing precisely the intention 
of an author is still so difficult that the conference be- 
tween the specialist and the author of a 10-page paper 
may take as much as an hour. By the time the conference 
is finished, however, the natural-language text has been 
perfected to the point at which it can be translated into 
the representation language without the loss of any in- 
formation the author considers essential. When the trans- 
lation algorithms operate upon the final natural-language 
text, they ordinarily require no human intervention. 
Whenever a problem of translation persists, however, it 
is made the subject of a conference between the system 
specialist and experts who specialize in translation algo- 
rithms. Over the years, these conferences have con- 
tributed much to the perfection of the translation algo- 

In this part of the work the language employed by 
the system specialist is essentially a language for con- 
trolling the operation of language algorithms, editing text 
(jointly with a colleague at a distant console), testing the 
logical consistency of statements in the representation. 
language, and checking the legality of information struc- 
tures and formats. The part of the language concerned 
with editing is to a large extent graphical. Both the sys- 
tem specialist and the author point to words and sen- 



tences in the text, move them about with the aid of their 
styli, insert or substitute new segments of text, and so 
forth. The language used in controlling algorithms is 
essentially standard throughout the procognitive system. 

When an incoming document contains many refer- 
ences to associated information structures, the task of the 
system specialist expands. He has to introduce the links 
that will connect the textual document with the associ- 
ated structures and the associated structures one with an- 
other. Inasmuch as the work we are discussing is done in 
a second-echelon center, this does not involve the actual 
introduction of the new information into the over-all 
body of knowledge, but — operation for operation — it 
amounts to almost the same thing. The main difference 
is that there is even more caution, more insistence upon 
verification in the top-echelon centers. In linking the 
various structures, the system specialist makes statements 
of the following kind: 

Table 3 is a two-dimensional matrix with 
alphanumeric row and column headings and 
with row and column marginal totals. The 
entries are floating-point decimal num— 
bers. Check the format. If it 1s all 
right, file it, and construct a bidirec— 
tvonal link’ to "Table 3" and the tite: 

The system will know, of course, to file it in the data base. 

Encountering a series of equations in a document, and 
seeing that they are sufficiently complete to constitute an 
_ operable computer routine, the system specialist makes 
a series of commands such as the following: 

Assign an arbitrary PROCOL independent 
routine label to these [He points] equa-— 
tions. ..Construct, a bidirectional, lank 
between the text and the routine. Assume 



ranges for the variables. Assume assign— 
ments of variables to axes. Display rela- 
tions on screen while running program. 

Run the routine. 

In this way, the system specialist checks the operability 
of the system of equations as a “PROCOL routine.” 
While observing the display, he may, for example, make 
some modifications in the ranges of variables, and substi- 
tute z for x as the independent variable. In such opera- 
tions, he uses the graph-control sublanguage that is stand- 
ard for the entire procognitive system. 

One of the main features of the language mechanism 
used by the system specialist is a built-in “understanding” 
of the several kinds of information structure that system 
specialists usually handle. When the information struc- 
ture is a matrix, certain operations, certain storage loca- 
tions, certain kinds of link, certain kinds of test, and cer- 
tain kinds of display are appropriate. When the informa- 
tion structure is an operable program, a different set of 
things is appropriate. When the information structure is 
a textual string, still different things are appropriate. The 
linguistic development that has made all this possible has 
been the development of an interpretive mechanism that 
examines the class memberships of the entities mentioned 
in the declarations, commands, and questions, deter- 
mines from those memberships what operations and inter- 
relations are appropriate and uses that information to 
guide its implementation of the instructions. 

The format of the illustrative commands given in the 
foregoing paragraphs is intended, of course, only to sug- 
gest. No such language is currently implemented on a 
computer. The suggestion is that such a language will in 
due course be implementable. The use of pronouns will 



cause no serious difficulty. Most of the power of the lan- 
guage stems from the interplay of the facts already men- 
tioned, (1) that the language mechanism “understands” 
the information structures that will be encountered in 
its work, and (2) that the operations to be performed 
upon the information structures have been carefully pro- 
grammed and associated with the verbs of the language. 

The design of a specialized language, according to this 
line of thinking, is in large part a matter of identifying 
the information structures and the fundamental opera- 
tions of the special domain of application. It includes also 
the formulation of a sufficiently sophisticated syntactic 
analysis to permit flexibility of expression and sensitive- 
ness of response to tense, voice, mood, and so forth. The 
language must, of course, be intimately correlated with 
the physical intermedium and with the repertory of proc- 
essing techniques that are available. “Techniques,” as used 
here, is not the same as “operations.” There may be sev- 
eral different ways to carry out a given operation. To the 
substantive user, the difference may appear only in speed 
or in cost. To the system specialist or the programmer, on 
the other hand, the selection of a particular technique 
may have profound implications. Selecting the wrong 
technique for the performance of a fundamental opera- 
tion might seriously handicap future development of the 
system. One of the main responsibilities of system special- 
ists, therefore, is to exercise good judgment about the 
selection of techniques. The selection of operations, on 
the other hand, is determined largely by the requirements 
of the task at hand. 

We have discussed one particular kind of task in which 
system specialists are involved. Let us now turn to an- 
other kind of task. It is part of what we have called “mull- 



ing over” the body of knowledge — looking for unde- 
tected relations within it and for possibilities of improv- 
ing its organization. Let us concern ourselves here with 
one small facet of that endeavor. 

Consider the work of a system specialist who is ex- 
ploring the possibility that there may exist, within a part 
of the body of knowledge as currently represented, a 
number of basic abstract correlations that have not yet 
been detected. Let us suppose that he has available the 
language and processing mechanisms capable of trans- 
forming patterns of information from the standard repre- 
sentation language into various other representations, 
based upon other information structures. By “informa- 
tion structures,” we refer, of course, to trees, lists, 
matrixes, relational nets, semantic nets, multidimensional 
space analogues, and so forth. The foregoing are cur- 
rently available examples, but during the next decades 
there should be great development — both in prolifera- 
tion and in sophistication — of information structures. 

The language employed by the system specialist 
handles the standard representation language approxi- 
mately the way a present-day query language handles a 
simple hierarchical file. The system specialist might say, 
for example: 

Designate as A, B, and C, respectively, 
the parts of the representation that con-— 
tain models involving DNA and RNA, models 
of transformational grammars, and 
information—compression codes. Transform 
each of the parts, A, B, and C, into 
"trie" representation, list-—structure 
representation, and semantic-—net represen-— 
tation. Within each of the latter, 
truncate each representation tentatively, 
retaining the highest-level 1,000,000 



bits. Now, consider subdivisions of the 
representation that are locally complete 
and approximately 10,000 bits in size. 
Correlate all the subrepresentations of A 
with each subrepresentation of B, and do 
the same for B and C and for C and A. 
Display all pairs of patterns that have 
correlations greater than 0.8. 

In the foregoing series of commands (which in prac- 
tice would have been interspersed with requests for step- 
by-step display of results), there were many points at 
which substantive knowledge of information structures 
and technical operations had to be “understood” by the 
language mechanism. Since many of the structures and 
most of the operations are of particular significance to 
system specialists, the organizing language contains spe- 
cific terms for them. 

System specialists have many other tasks besides the 
two that have been discussed. They preside over the in- 
troduction, into the top-echelon representation of the 
body of knowledge, of those contributions that are locally 
organized and tested in the second-echelon centers. They 
call to the attention of experts in the various substantive 
fields the correlations and systematizations they find 
plausible in their continual examination of the body of 
knowledge, and they work with the substantive experts 
in reorganizing the representations of substantive areas. 
They seek continually to improve the representation lan- 
guage. Approximately once each decade they have to 
adapt the representation to a new and improved hard- 
ware base. In so doing, they work closely with the system 
programmers. Indeed, there are some system specialists 
who are programmers, and some programmers who are 
system specialists. There is no sharp line between the two, 



any more than there is a sharp line between system spe- 
cialists and specialists in substantive areas of science and 

V User-oriented languages 

The existence of so very many subfields of science and 
technology, each with its local jargon, its own set of fre- 
(quently occurring operations, and its own preferences for 
data structures and formats, makes it necessary to have 
many different user-oriented languages. However, there 
is a homogeneity that underlies the diversity. We have 
seen the basis for this underlying homogeneity in our dis- 
cussion of programming language and organizing lan- 
guage. The homogeneity is inherent in the fact that there 
are only a few syntactic classes, only a few dozen infor- 
mation structures, and only about a hundred kinds of 
operation — though there are very many different opera- 
tions — in the entire spectrum of activities of the pro- 
cognitive system. The various field-oriented and problem- 
oriented languages employed by the substantive users of 
the system are therefore all related, one to another, and 
they are similar in their basic essentials to the languages 
used by the programmer and the system specialist. 

One of the tasks of the specialist that is relevant in the 
present connection is to keep the user-oriented languages 
compatible with one another. User-oriented languages 
naturally tend to develop in their own particular direc- 
tions, and every effort is made not to impede such de- 
velopment when the needs are truly special. It usually 
turns out, however, that consultation between a substan- 
tive user and a system specialist reconciles achievement 
of the goal of a new specialization with preservation of 
order within the over-all system. This is true because the 



substantive user only rarely invents a new linguistic tech- 
nique that is superior, for his purposes, to every other 
technique already established within the system and be- 
cause any new and useful one that he does invent soon 
finds application in other parts of the system. 

The interaction between a substantive user and the 
procognitive system may be intellectually as deep as the 
user’s penetration of his field of study, but it should be 
simple. It tends to be simple because there are only a 
few kinds of action for the user to take at his console. 
Almost everything posted on a display, for example, is 
put there by the computer. If the user wants to make a 
mark or turn on a light, he tells the computer what, 
when, and where, and the computer proceeds to carry 
out the act. 

In order to match the small set of control actions a 
man can take with the vast assortment of things there 
are to be done in the world of the procognitive system, it 
is necessary to take advantage of the concept of deter- 
mination by local context that is so highly developed in 
the natural languages. In the system of user languages, 
the first step in that direction is the selection of a sub- 
language — a _ field-oriented or problem-oriented lan- 
guage designed to handle the kind of task with which the 
user is concerned. The second step, taken within the sub- 
language, is to enter a “mode.” We have already touched 
briefly on modes of display associated with the display 
screen and the light pen. When the pen is used merely 
_as a pointer, the meaning of its message is conveyed 
partly by the location of the spot to which it points. How- 
ever, the meaning is determined also by the nature of 
the display that is currently being presented and the par- 
ticular details of that display. The part of the determina- 



tion that is associated with the nature of the display is 
“mode” determination. 

The simplest on-line man-computer interaction sys- 
tems we know of have only two modes: a “control mode” 
and a “communication mode.” In the control mode, the 
signals the operator directs to the computer select one or 
another of a set of subprograms. In the communication 
mode, the signals merely enter a buffer space in the com- 
puter’s memory, and what the computer then does to the 
signals depends upon which subprogram is brought into 
action. For example, in an on-line “debugging” system 
called “DDT,” the programmer, trying to find out what is 
wrong with his program, might type: 


The solidus is a control-mode signal that means “I have 
just transmitted to you three characters or their equivalent 
that constitute the label of a register in memory. When 
you have received them, look them up in your address 
table, examine the register with the corresponding ad- 
dress, and type out in octal notation the number you find 
in it.” In response, DDT would type the number: 

sto/ 123456 

If the programmer wanted to change that number to 
123321, he would simply type 123321 and press the car- 
riage-return key. The carriage return is another control 
character. It says, “Substitute the number now in the 
buffer for the old one, and do nothing else about this 
register unless it is mentioned to you again.” The pro- 
grammer might then check to see that the new number 
had in fact replaced the old. To make the check, he 
would press the period key and the solidus key. (The 



period is not a control character. It is a “pronoun” that 
stands for the last-mentioned register label.) The com- 
puter would then respond with the number in the second 

sto/ 123456 123321 
cuff 123321 

It is evident from the foregoing that DDT uses a short- 
hand notation that is much less explicit than the notations 
used in the examples given in connection with program- 
ming language and organizing language. This is partly 
because the language of DDT is not very far removed 
from machine code, whereas the hypothetical languages 
are high-level languages. Nevertheless, there is an ad- 
vantage to the terseness of the DDT control language 
that should not be lost to increasing sophistication. This 
matter will be discussed in the section on Representation 
Language. The point here is that one part of what the 
user types is essentially an instruction to the system, tell- 
ing it what to do to an object, and another part is the 
object on which the system is to act. This fundamental 
distinction will probably never disappear. However, it 
will not long remain as simple as it is in DDT. 

One of the first necessary complications of the distinc- 
tion between the control mode and the communication 
mode has already appeared in several on-line languages 
and is also found in DDT. It is the “execute” command. 
The execute command instructs the system to consider 
the object communicated no longer as an object to be 
_ processed but as a command to be implemented. The 
computer therefore executes the communicated message 
as an instruction. This corresponds approximately to re- 
moving the quotation marks from a string of characters 
of ordinary text. Several groups have recently built into 



ALGOL the capability of removing the quotation marks 
from a string of characters and executing the string as an 
ALGOL procedure. 

We may expect a continual development of on-line in- 
teraction languages until the residual distinction between 
control and communication mode is carried partly by the 
syntactic structure of statements and partly by context. 
In the work with programs discussed earlier, for example, 
the system programmer first examined the programs, 
treating them as objects, and then tested them, causing 
the system actually to carry out the procedures. 

The convenience of having local context or mode im- 
plicitly define terms and particularize procedures for him 
may to some extent be countered by the responsibility, 
thrust upon the user, of continually keeping track of the 
prevailing mode. However, one of the basic facts of man- 
machine interaction is that, although the responsibility 
for keeping track of mode or situation causes great diffi- 
culty in monitoring, it causes almost no difficulty in truly 
symbiotic interaction. In truly symbiotic interaction, the 
human partner is always active, always involved in di- 
recting, always “ahead of the game.” In monitoring, on 
the other hand, he tends just to sit there, waiting for an 
alarm to alert him to action. When the alarm goes off, 
he does not know what the situation is, and it is difficult 
or impossible to find out in time to do anything effective. 
It does not help the monitor much to display to him the 
developing situation, either in summary or in detail, for 
it is almost impossible for him to think ahead constantly if 
his thinking has no effect on what happens. In our con- 
ception of man-computer interaction in procognitive sys- 
tems, however, the man is no mere monitor. He is a part- 
ner — indeed he is usually the dominant, leading partner. 



On-line interaction introduces into the language pic- 
ture the possibility of “conversation.” This possibility, to- 
gether with the need to bring on-line languages abreast 
of conventional programming languages, opens an invit- 
ing field to research and development. It seems to us de- 
sirable to move rapidly beyond the simple dichotomy be- 
tween the control and communication modes and to 
develop a syntax in which it will be possible to express 
commands, state facts, and ask questions in any conven- 
ient sequence. 

The most economical approach to that objective ap- 
pears to be to direct all the operator’s signals, except one 
special “terminator” signal, into a buffer. The operator 
can put as long a string as he likes into the buffer. When 
he wants the signals to control action or to be acted upon, 
he presses the terminator key. The computer at once 
recognizes the terminator and calls a translator, a pro- 
gram that sorts the contents of the buffer into commands 
and data and puts them into a format determined in part 
by the syntax of the language and in part by the needs 
of the programs to be controlled or employed. The trans- 
lator initiates the requested actions by executing the first 
command. From then on, processing follows the course 
determined by the commands in interaction with the pro- 

What is needed, we believe, is a synthesis of the good 
features of the several approaches that have been men- 
tioned in the foregoing discussion. In order to control 
- complex processes, an on-line interaction language must 
have a sophisticated syntax and a large vocabulary. At 
the same time, to cater to the user’s convenience, it should 
minimize the requirement for complex or manifold con- 
trol actions, and it should encourage the kind of con- 



vergence upon understanding achieved in conversation. 
To facilitate learning, and to promote efficient utilization 
of programs, on-line interaction languages should be 
compatible with one another and should fit together into 
a coherent system. 

Representation language 

The representation of information for storage and re- 
trieval has already been discussed at length. Here we 
merely reiterate our conviction that both formalization 
and sophistication of language seem important for effec- 
tive representation and efficient processing of a large 
corpus. We suspect that “the” representation language, if 
a single representation language ever becomes dominant 
over all its competitors, will be essentially a language 
system. It will have several sublanguages, specialized for 
different applications. Some of them may have as many 
syntactic categories as the natural languages and will dis- 
tinguish among several thousand sharply defined seman- 
tic relations. Some of them may “understand” the com- 
plex interrelations among the semantic relations and the 
syntactic categories and deal competently with several 
dozen clearly defined information structures. Others will 
be simpler but less powerful. 

We must distinguish between a language or sub- 
language and its implementation through computer pro- 
gramming. One language or sublanguage may have sev- 
eral implementations, differing in compactness of packag- 
ing and speed of processing, but yielding the same answer 
to a given question and the same solution to a given prob- 
lem. There will probably be a demand for two or more 
implementations of certain sublanguages in the procogni- 
tive system. 



The over-all language will be a system because all the 
sublanguages will fall within the scope of one metalan- 
guage. Knowing one sublanguage will make it easier to 
learn another. Some sublanguages will be subsets of 
others. There will be translators, as suggested earlier, to 
convert sets of data or domains of knowledge from one 
representation to another. One sublanguage will resonate 
with one discipline or problem, another with another. 
But the whole apparatus of representation will be inter- 
nally consistent. It will be a coherent system — if the day 
ever arrives. 

Meanwhile, every effort should be made to find a way 
out of the present chaos of fragmentary and incompatible 
schemes for representation. Standardization of terms and 
formats will doubtless help. The main hope, however, 
appears to lie in the development of community comput- 
ing systems. The economic impracticality of having a dif- 
ferent system for every user will force convergence. There 
will be an active market place and a strong incentive to 
form coalitions. Perhaps one coalition will become a 
monopoly. Monopoly should not be allowed to stifle re- 
search on languages and representations, but it should 
be encouraged to foster coherence within the operating 


In a sense, the whole of the procognitive system and its 
use is an adaptive, self-organizing process. The adaptive, 
self-organizing system includes, of course, the specialist 
personnel and the substantive users. One of the main 
goals to be sought through adaptive self-organization is 



recursive: it is increased effectiveness in growing, de- 
veloping, adapting, and organizing. The other main goal, 
of course, is increased effectiveness in serving the sub- 
stantive users. 

Underlying the global aspect of adaptation and self- 
organization, there must be continual adaptation of the 
system to meet the needs of its users and a continuing 
development, on the part of the users, of the ability to 
take advantage of the services offered by the system and 
to improve the system in the process of using it. 

We see the topic thus introduced as a very large and 
important one, but one that still remains almost unex- 
plored. The prospects range all the way from simple 
adaptations that we know how to achieve, such as adjust- 
ment of the explicitness of user-oriented languages to the 
level of experience of the individual user, to regenerative 
self-organization of the procognitive system through its 
use by schools, colleges, and universities in education and 
in research. We have done enough work in computer- 
aided teaching and computer-facilitated study to sense 
that a procognitive system might contribute greatly to 
education by increasing the rewards to be won through 
intellectual effort. Let us end this section in a lower key, 
however. The console of the procognitive system will 
have two special buttons, a silver one labeled “Where am 
I?” and a gold one labeled “What should I do next?” 
Any time a user loses track of what he is doing, he can 
press the silver button, and the recapitulation program 
will help him regain his bearings. Any time he is at a total 
loss, he can press the gold one, and the instruction pro- 
gram will explain further how to use the system. Through 
either of those programs, the user can reach a human 




roy zee 

presd gm: di 

Pe i hee rc 




Part II introduces and summarizes briefly 13 elements 
of the program of exploration into uses of computers that 
constituted the major part of the two-year study. Chap- 
ter 5 is a survey of syntactic analysis by computer. Chap- 
ter 6 deals with quantitative aspects of files and text that 
bear upon the feasibility and efficiency of computer proc- 
essing of library information. Chapter 7 describes a prom- 
ising method for evaluating retrieval systems. Chapter 8 
contrasts document-retrieval with fact-retrieval and ques- 
tion-answering systems. Chapter 9 describes eight efforts 
to develop, test, and evaluate computer programs that 
perform, or techniques that facilitate, library and procog- 
nitive functions. 

i imaA4% 

* 7 = - 6 . AS . i me _ 
Wi4iAO3 1O o2b) Desi eaaese 


Syntactic Analysis of 
Natural Language by Computer 

THE RELEVANCE of automated syntactic analysis to li- 
brary procognitive systems lies in machine processing, 
and in eventual machine “understanding,” of natural- 
language text. There is no thought that syntactic analysis 
alone — whether by man or machine — is sufficient to 
provide a useful approximation to understanding. On the 
other hand, there is no doubt that appreciation of the 
syntactic structure of natural-language text is a part, and 
an important part, of the over-all problem. Accordingly, 
Bobrow (1963) surveyed the work that has been done, 
and is being done, toward automation of syntactic analy- 
sis of English. 

The efforts to automate syntactic analysis have been, 
essentially, efforts to implement various theories of gram- 
mar through the preparation of computer programs that 



operate on natural-language text in the form of strings 
of encoded alphanumeric characters. The output of a suc- 
cessful syntactic-analysis program is one or more sets of 
assignments of the words of a sentence to grammatical 
categories (“parts of speech”) plus, for each set of as- 
signments, a representation of the grammatical structure 
of the sentence. 

The grammars that have been used as bases for syn- 
tactic-analysis programs include dependency grammars, 
phrase-structure grammars with continuous and with dis- 
continuous constituents, predictive grammars, string- 
transformation grammars, and phrase-structure transfor- 
mational grammars. The main characteristics of, and dif- 
ferences among, these grammars are set forth in Bobrow’s 
report. Although it is too early to say with assurance 
which of them is (or are) best for our purposes much 
expert intuition favors the phrase-structure, transforma- 
tional approach. 

Many of the programs that have been successful in 
making syntactic analyses have depended upon a distinc- 
tion between “function words” and “content words.” The 
distinction is a simple and familiar one. Function words 
are«words such as “and;? “or “to, strom; “as. ante 
“the.” “neither,” “nor. cif, and: “whether. Content 
words are words such as “grammar,” “equivalent,” “struc- 
tural,” “diagram,” “sentence,” “minimum,” “weekly,” 
“accept,” and “develop.” There are, of course, many more 
types of content words than of function words. It is there- 
fore reasonable to store in a computer memory very 
detailed descriptions of the functions, characteristics, 
idiomatic uses, and so forth, of the function words. The 
successful analytic programs have, in addition, employed 
dictionaries containing, for each content word, the gram- 

39 66 



matical categories into which it is normally expected to 

It is the intention here, not to give a complete sum- 
mary of Bobrow’s report, but rather to relate the idea 
of syntactic analysis by computer to the over-all picture 
set forth in Part I. Perhaps the best way to do that is to 
show in diagrammatic form some of the analyses that 
are described in detail by Bobrow. 

The notion of “dependency” gives a direction and a 
hierarchical structure to syntactic relations. Adjectives de- 
pend on nouns, nouns depend on verbs and prepositions, 
adverbs and auxiliary verbs depend on main verbs, prepo- 
sitions depend on the words modified by their phrases, 
and so forth. The system of dependency can be repre- 
sented diagrammatically in the way illustrated for the 
sentences, “The man treats the boy and the girl in the 


Dependency Analysis 

and “The man at the door turned the light out.” 
man light out 
BS / 
the at_ the 


Dependency Analysis 


This second sentence will be used to illustrate other gram- 
mars also, in order to display similarities and differences. 

Computer programs capable of implementing depend- 
ency analysis have been developed or described by Hays 
(1962) of the RAND Corporation, by Kelly* of the 
RAND Corporation, by Gross (1962) of M.I.T., by 
Klein and Simmons (1963) of the System Development 
Corporation, and by several Russian workers, including 
Moloshnava (1960) and Andreyev (1962). 

In a phrase-structure analysis that assumes continuous, 
immediate constituents,7 the diagrammatic representation 
is again treelike, but the nodes of the branching structure 
are, at all levels except the lowest, syntactic or gram- 
matical categories. At the lowest level, the actual words 
of the sentence appear. An immediate-constituent analy- 
sis of “The man ate the apple” is shown in the diagram. 


Noun Phrase Verb Phrase 

aN fo ee 

ff Noun Verb Noun Phrase 
os Ue 

| T Noun 

the man ate the apple 

Immediate-Constituent Analysis 

The analysis starts at the bottom of the diagram with the 
string of words and proceeds to discover a superstructure 
consistent with their grammatical class memberships. 
However, the analysis may involve trial-and-error differ- 

* H. Kelly, Personal communication, September 1963. 

+ Immediate constituents are parts that are separated by the first 
step of an analysis, ie., that are encountered immediately; they are 
to be contrasted with ultimate constituents. Continuous constituents 

are parts whose subparts are contiguous; continuous constituents are 
to be contrasted with discontinuous constituents. 



entiation downward from assumed categories to strings 
of words. 

The analysis substitutes for “Sentence” the two cate- 
gory names, “Noun Phrase” and “Verb Phrase.” It then 
substitutes for “Noun Phrase” the category names “Defi- 
nite Article” (or, since there is only one definite article in 
English, the symbol “T”) and “Noun.” For “Verb 
Phrase” it substitutes “Verb” and “Noun Phrase.” It is 
then in a position to substitute actual words for three of 
the category names. “Noun Phrase,” however, has to be 
passed through one more stage of analysis before the 
substitution of actual words can be made. The result of 
the analysis — the diagram — displays the roles of the 
individual words of the sentence and, in addition, shows 
how the several roles are interrelated. 

The next diagram illustrates immediate-constituent 

Noun Phrase Verb Phrase 
Verb Phrase — 
Noun Phrase Prep Phrase Particle* Particle 

f A vi ae eit ae 

T Noun Prep Phrase Particle Phrase Particle 
Lon x 
T Noun T Noun 

the man at the door turned the light out 

Immediate-Constituent Analysis 

* Read the minus sign (not hyphen) as “minus.” 

analysis of “The man at the door turned the light out.” 
In the foregoing example, the analysis proceeded in a 



succession of binary branchings. An alternative formula- 
tion makes use of multiple branching to produce coordi- 
nate structures with fewer levels. Bobrow compares bi- 
nary structure and coordinate structure in the phrase, 
“the old black heavy stone.” 

Noun Phrase Noun Phrase 
We Noun Phrase ee / reese 
IN T Adj Adj Adj Noun 
ji Noun Phrase | | 
a the old black heavy stone 
Adj Adj Noun Phrase 

Adj i 
the old black ee stone 

Binary structure Coordinate structure 

Immediate-Constituent Analysis 

Computer programs capable of making immediate- 
constituent analyses have been developed or described by 
Robinson (1962) of the RAND Corporation, Cocke* of 
the International Business Machines Corporation, and 
Klein (1963) of the System Development Corporation. 

Kuno and Oettinger of Harvard (1963) have de- 
veloped extensively the technique of predictive analysis 
advocated by Rhodes (1961) of the National Bureau of 
Standards. Predictive analysis takes advantage of the fact 
that, once he has heard the beginning of a sentence, the 
listener can rule out many of the myriad syntactic pat- 
terns into which sentences are, a priori, capable of falling. 
Predictive analyses keep track only of the alternative in- 
terpretations that are consistent with the part of the 
sentence that has already been analyzed. At the very be- 
ginning, there are usually but few alternatives, for then 

* John Cocke, Personal communication, September 1963. 



the interpretations are not differentiated. In the mid- 
course of an analysis, there may be many possible ways 
in which the sentence can go. At the end, however, the 
analysis should converge upon one pattern of gram- 
matical categories, or, at any rate, upon a set of patterns 
among which a choice can be made on the basis of seman- 
tic interpretation of the sentence itself and of its context. 
(Unfortunately for the prospects of machine “understand- 
ing,” “context” must embrace both linguistic context and 
circumstantial or nonlinguistic context.) The Kuno- 
Oettinger programs determine all the alternatives. Re- 
lated programs developed by Lindsay (1963) of the Uni- 
versity of Texas find only one syntactic pattern but 
provide diagnostic information on the basis of which it 
is possible to clear up misinterpretation through “post- 

A minor problem for machine analysis is introduced by 
the fact that two words may together fill a grammatical 
category without being contiguous in text. This problem is 
not faced squarely by immediate-constituent grammars. 
In discontinuous-constituent grammars, however, the 
problem is recognized, and a special linkage is introduced 
to connect the separated parts. The diagram illustrates an 
analysis of “He called her up.” 

Sentence ert 
ve ~ =~ R 
Pronoun / Verb Phrase NN e 
We Ne <i 
iy Pronoun Particle 
he called her up 

Discontinuous-Constituent Analysis 



The linkage from “Verb” to “Particle” connects “up” to 
“called,” from which it has been separated by “her.” The 
diagram shows a discontinuous-constituent analysis of the 
standard sentence, “The man at the door turned the light 


bine hare the eats 

Noun Phrase I Verb Ne << 
Mike | \ 
| x 

Noun Phrase Prep Phrase Verb an Phrase Particle 

Peat eee 

T Prep Phrase Particle T Noun 
T Noun 
the man at the door turned the light out 

Discontinuous-Constituent Analysis 

For the purposes of library procognitive systems, the 
most important problem in this subject area may well be 
one raised by Chomsky (1956, 1957). Chomsky was 
concerned that two expressions of the same idea, such as 
“The man drives the car” and “The car is driven by the 
man” do not have similar phrase structures and do not 
yield similar diagrams when analyzed in the ways we have 
been discussing. Chomsky handled this problem by set- 
ting up “transformation rules” that transform one sen- 
tence into another, or combine n sentences into one, or 
subdivide one sentence into n sentences. For example, one 
transformation takes a sentence from active voice to pas- 
sive voice. Another transformation combines two single- 
clause sentences into a compound sentence. With trans- 



formation rules, one can build up complex syntactic struc- 
tures from simple ones or analyze complex syntactic 
structures into simple ones. Bobrow gives an example in 
which the simple statements 

X’s are the airfields. 

The airfields are in Ohio. 
The airfields have runways. 
The runways are long. 
Two miles is long. 

cee ae 

are derived from the question, “What are the airfields in 
Ohio having runways longer than two miles?” Analysis 
and synthesis based on such transformations will surely 
be important for machine-aided organization of the body 
of knowledge. 

The transformational-grammar approach handles the 
discontinuous-constituent problem neatly. A transforma- 
tion changes “turned the light out” into “turned out the 
light.” As the diagram shows, the analysis then proceeds 
without any difficulty. 

Noun Phrase Verb Phrase 

Noun Phrase Prep Phrase Verb Noun Phrase 

deed aon =f \ 

T Noun Prep Phrase Particle Particle T Noun 


T Noun 

e man at the door turned out the light 

Intermediate Stage in Chomsky’s Transformational Derivation 



Walker and Bartlett (1962) of the Mitre Corporation 
are developing a parsing program based on a transforma- 
tional grammar. 

Harris (1962) and his associates at the University of 
Pennsylvania have developed a method of syntactic analy- 
sis that is intermediate between constituent analysis and 
transformational analysis. Although their method does 
not lend itself to representation by a tree diagram, a rough 
idea of its approach is conveyed by relating some of its 
terms to our standard sentence. In the sentence, the 

‘sttime” ‘center’ “1s... mais.) turned Out™ tee 
light.” One “the” is the “left adjunct” of “man,” and the 
other i is the “left adjunct” of “light.” “At the door” is the 
“right adjunct” of “man.” This “string-transformational 
grammar” has been implemented in the form of com- 
puter programs. The “Baseball” program developed by 
Green and associates (1961) at the Lincoln Laboratory 
—a program capable of answering questions about the 
outcomes of the baseball games played in the major 
leagues during one season — has a methodological kin- 
ship to the Pennsylvania work. 

From Bobrow’s survey, it is clear that automation of 
syntactic analysis is possible. Indeed, there are several 
operating syntactic-analysis programs. It is equally clear, 
however, that syntactic analysis is only a part — and per- 
haps, relatively, only a small part — of the over-all prob- 
lem. The fact is that even the best analysis programs (ex- 
cepting Lindsay’s, which arbitrarily limits itself to a single 
syntactic pattern) produce discouragingly many alterna- 
tive patterns. Selection among the alternatives has to be 
made on nonsyntactic grounds, and there has not been 
much progress toward automation of that selection. 
Furthermore, it is evident that one is a very long way 



from understanding what is being said when all he knows 
is the pattern or structure of syntactic categories into 
which the words of the message fit. 

The question was raised in Part I, whether it is desir- 
able, in formulating the basic concepts of this field, to 
separate syntactic and semantic factors into the two in- 
sulated bins of a rigid dichotomy, or whether, as subtler 
and subtler distinctions are made in the process now 
called syntactic analysis, that process will start to become 
semantic as well as syntactic. Although we are not in a 
position to decide, one way or the other, on that question, 
we have an intuitive feeling that the latter is more promis- 
ing as a line of development. We should, in this connec- 
tion, refer to work that we regard as extremely promising, 
work being carried out by F. B. Thompson and his col- 
leagues (Thompson, 1963). 



Research on Quantitative 
Aspects of Files and Text 

Two STUDIES by Grignetti (1963a, 1964) deal with quan- 
titative aspects of representations of information in digital 
memories. The first study concerns the average length of 
representations of descriptions of documents or, in greater 
generality, the average length of representations of 
“terms” (e.g., of descriptors associated with documents). 
The second concerns the informational measure “en- 
tropy” —or, to look at it the other way around, the 
redundancy — of English text considered as a string of 
_ words. Thus both studies bear upon the amount of mem- 
ory required to store library information: the first with 
indexes, the second with actual text. 



From one point of view, the over-all organization and 
concept of a library or of a procognitive system is a more 
important thing to grasp, or to improve, than is the effi- 
ciency of a low-level “detail” function. On the other 
hand, a few functions that appear from one point of view 
to be mere details, to occupy low levels in the over-all 
system, are seen from another point of view to be both 
basic and ubiquitous. One of these functions that looks 
like a technical detail from one standpoint and like some- 
thing very basic and general from another is the encoding 
of elements of information for storage in a digital mem- 
ory. Let us, for the purposes of this discussion, adopt the 
point of view from which it seems important. As soon as 
we do that we may be prepared to examine subcategories, 
and one of the most conspicuous of these is the subcate- 
gory that includes catalogues and indexes. The study to 
be summarized deals with such files of information. 

Perhaps the best schema to keep in mind while think- 
ing about this problem is the schema of an index consist- 
ing of the names or numbers of the documents in a collec- 
tion and, associated with each name or number, a list of 
terms or descriptors that characterize the corresponding 
document according to some coordinate indexing system. 
The problem under consideration is how to encode the 
terms. The object is to be economical in the use of storage 
space and, at the same time, to make it easy for a com- 
puter to decode the representation and determine the 
names or numbers (or addresses) of specified documents. 

One of the most widely used techniques for represent- 
ing terms is simply to spell them out in full or to record 
readable abbreviations of them. The direct way to encode 



such representations for storage in digital memory is to 
assign a binary code to each character in the character 
ensemble and to store the binary code patterns. That 
technique has the disadvantage of using much more mem- 
ory space than is necessary. This kind of inefficiency is 
the subject of the study to be summarized in the next 

A technique that is more economical of memory space 
is to number all the terms that may be used and to repre- 
sent in memory in association with each item, not the 
corresponding terms themselves, but the numbers that 
were assigned to them. Since there are sometimes several 
terms per document, it is important not to let the numbers 
that represent different terms run together in such a way 
as to preclude subdivision of the over-all representation 
into its parts. The encodings of the sets of terms associ- 
ated with different documents must also be kept separa- 
ble. In the past, people have kept codes separable either 
by using special characters as separators or by adding 
enough leading zeros to the short codes to make all the 
codes the same length. (Fixing the length of the codes 
takes care only of separations within the set of terms asso- 
ciated with a given document, and not of separations be- 
tween the sets of terms associated with different docu- 
ments, but we may for the sake of simplicity limit our 
consideration to the problem of intraset separation. ) 

The prevailing opinion has been that the use of separa- 
tors leads to more compact files than the use of fixed- 

‘length codes. The first thing Grignetti did was to analyze 
that comparison (1963a). It turned out that, on the 
average, and under certain reasonable assumptions, the 
fixed-length code is actually shorter than the variable- 
length code plus separator. If one thinks about this ques- 



tion with simple, schematic examples in mind, he is likely 
to doubt this conclusion of Grignetti’s and to agree with 
the prevailing opinion that Grignetti finds incorrect. How- 
ever, Grignetti’s conclusion becomes quite obvious as 
soon as attention is focused upon large filing systems in 
which the list of legal terms is long. Consider, for ex- 
ample, that, of a list of nearly 1000 terms, almost 90 per 
cent would be represented by three-digit codes. About 9 
per cent would require one leading zero, and about 1 per 
cent would require two leading zeros to bring them up to 
the “fixed” length of three digits. 

In his work on coding efficiency, Grignetti also ex- 
amined the notion that products of prime numbers might 
provide the basis for an effective coding system for the 
purpose we are considering here. In such a system, each 
term would be assigned a prime number, and the set of 
terms associated with a particular item would be repre- 
sented by the product of the prime numbers associated 
with the members of the set. Grignetti was able to show 
conclusively that the prime-number code is an inefficient 
code, less good than either variant of the elementary sys- 
tem considered in the foregoing paragraphs. 

The preliminary inquiries just described led Grignetti 
to look for a truly efficient coding system for terms 
(1964). He found one. He calls it the “combinational 
code.” It is fairly easy to construct a combinational code. 
First, one has to decide the maximum number of terms 
that will be associated with an item, say, five. Second, he 
examines the list of legal terms, numbers the individual 
terms, and makes up all the possible combinations of 
term numbers, taken five or fewer at a time. Third, he 
orders the numbers corresponding to the terms of each 
combination (each set of five or fewer) in a sequence of 



increasing magnitude. Fourth, he reorders the list of com- 
binations according to a criterion that takes into account 
primarily the magnitude of the largest number in the 
subset but also, secondarily, the number of numbers in 
the subset. Finally, he assigns integers in increasing se- 
quence to the members of the reordered list. Grignetti 
gives an analytical procedure for encoding and decoding. 
He points out that the procedure does not have to be 
changed, and that existing code numbers do not have to 
be altered, when new terms are added to the list of legal 
terms. Finally, he shows that the combinational code is 
the shortest possible code. 


Grignetti’s interest was attracted to the question of the 
representational efficiency of direct encodings of the 
strings of words that constitute text by the consideration, 
discussed in Part I, that storage of the body of knowledge 
in processible memories will be an important basis for 
procognitive systems (Grignetti, 1964). In order to esti- 
mate the representational efficiency, it is natural to com- 
pare determinations of the number of bits required to 
store a typical segment of text with estimates of the actual 
(Shannon-Wiener) information content of the text. The 
classical estimate of the information measure of a typical 
word of text is the one made by Shannon in 1951 on the 
assumption that the frequency of occurrence of word 
types is sufficiently approximated by Zipf’s famous law. 
Shannon’s estimate of the information measure, or en- 
tropy, was 11.82 bits per word. However, Bemer (1960), 
using roughly the approximation that Shannon used, cal- 
culated that words could be stored, on the average, in 



10.76 bits of memory space — with the aid of a com- 
pression code that was obviously not optimal. That led 
Grignetti to examine Shannon’s results closely. 

By a method slightly different from Shannon’s, a 
method that seems straightforward and in which no flaw 
has been detected, Grignetti found the information meas- 
ure to be 9.7 or 9.8 bits per word. 

From one point of view, the difference, which is only 
2 bits per word, does not seem likely to have much prac- 
tical significance. From another point of view, however, 
the important question is whether or not further work on 
encoding of text seems to be intellectually attractive. If it 
seems attractive, then it is possible, and perhaps likely, 
that a coding scheme will be found that is highly efficient 
in use of memory space and, at the same time, economical 
in respect of processing. On the other hand, if further 
work is not intellectually attractive, there is not likely to 
be an increase in either the efficiency of the use of space 
or the economy of encoding and decoding. Perhaps the 
2 bits per word will have a stimulating effect. Clearly, the 
point on which study should now concentrate is the sim- 
plification of encoding and decoding. 



A Measure of the Effectiveness of 
Information-Retrieval Systems 

larly simple schema is appropriate. The system is a “black 
box” that contains a collection or set of items and that, 
from time to time, either spontaneously or in response to 
a request, offers a subset of its contents to one of its sub- 
scribers and withholds the complementary subset. An 
item may be thought of as a document. A subscriber may 
be thought of as simply a criterion: to the subscriber, an 
item is either pertinent or not pertinent. If one considers 
a single item of the collection and focuses his attention 
upon a particular occasion — a particular request from 
a subscriber or a particular spontaneous offering by the 
system — he sees that the performance of the system may 
be described simply by placing a tally mark in a two-by- 
two contingency table: 



R means that the item was retrieved (offered); R, not 
retrieved; P means that the item was pertinent (or would 

have proved pertinent if offered; P, not pertinent. The 
tally mark indicates that the item in question was re- 
trieved but did not meet the subscriber’s criterion of per- 

It is natural to proceed from examination of the indi- 
vidual case to study of a large sample of cases. One then 
accumulates tally marks over the sample, counts the 
marks, and replaces them with numbers, i.e., with abso- 
lute frequencies or relative frequencies of occurrence. He 
can say, for example, that the system made a “hit” (re- 
trieved a pertinent item) in 0.40 of the retrievals of indi- 
vidual items, a “miss” (withheld a pertinent item) in 
0.20, a “false drop” (retrieved a nonpertinent item) in 
0.10, and a “pass” (withheld a nonpertinent item) in 
0.30. Note that only two of the relative frequencies are 
independent, for the sum over the four categories must be 
unity, and the fraction of the items that meets the sub- 
scriber’s criterion of pertinence is assumed to be fixed. 
Note, also, that there has to be some way to ascertain the 
pertinence or nonpertinence of withheld items. 

Investigating the problem of evaluation of effectiveness, 
Swets (1963) found that eight of the ten studies that met 
his criterion of pertinence reduced the analysis to two-by- 
two contingency tables. However, in none of the studies 



was advantage taken of the fact that evaluative pro- 
cedures have been developed for, and found useful in, 
other fields of application (e.g., radar, sonar, psycho- 
physics) in which performance may be summarized in 
two-by-two contingency tables. Swets therefore adapted 
some of the apparatus of statistical decision theory to the 
information-retrieval context and proposed a measure of 
merit, a measure that quantifies the ability of the system 
to maximize the expected value (“payoff”) of a retrieval 
trial, ie., of an offering or withholding of an individual 
item on a particular occasion. The measure takes into 
account the relative frequency and the utility (value 
minus cost) of each of the four categories in the two-by- 
two table. We shall not review here Swets’s explanation 
of its derivation. Let it suffice to say that an assumption 
of normality of distribution is involved, that the measure 
is based on maximum-likelihood statistics, and that, given 
the relative frequencies of hits and false drops in a par- 
ticular sample, one can read the value of the measure 
from an available table or graph. 

The measure is simple, convenient, and appropriate. It 
gives definite meaning to the concept, the “basic discrimi- 
nating power” of an information-retrieval system. The 
measure clearly separates discriminating power from mere 
willingness to yield output, thus avoiding a confusion 
that has been rife these last several years and that ap- 
pears to be at the root of many informational difficulties. 
Moreover, the measure brings with it a well-developed 
system of procedures that facilitate analysis and interpre- 
tation of data. 

We expect Swets’s measure to prove useful in evalua- 
tion of information systems. The main obstacle may lie 



in determination of the pertinence of withheld items. That 
obstacle is wide. It causes trouble for all the approaches 
to evaluation of performance in retrieval of information 

from large collections. 



Libraries and 
Question-Answering Systems 

MaARILL’s (1963) REPORT, “Libraries and Question- 
Answering Systems,” laid the groundwork for subsequent 
research on question-answering systems. The report con- 
sists of three parts: 

1. Two Concepts of a Library 
2. Question-Answering Systems 
3. Semantic Nets: Informal Introduction 

The first of the “two concepts of a library” is a schema- 
tization of a present-day library. In the schema, the li- 
brary consists of the collection of documents, the “tag” 
system (index, catalogue, terms, etc.), and the retrieval 
system that makes use of the tags to retrieve desired docu- 
ments. The contributions that technology can make 
within this first concept are acceleration of document 



handling, automation of the process of “tagging” (assign- 
ment of terms to documents), and improvement of the re- 
trieval process. Marill argues that this first concept, in- 
strumented on a modest scale, would yield unsatisfactory 
results, and that, carried to its logical limit, it would be 

The second concept of a library, according to Marill, 
is one in which the primary function is to provide not 
documents but information. The “system” of the second 
concept will be able to “read” and “comprehend” the 
documents themselves and not merely their tags. It will 
have a high capability for organizing the information in- 

It will be able to accept questions worded in natural 
English. If it has the requisite information available, it 
will answer the questions in natural English. Thus Marill 
advocates a very sophisticated procognitive system. He 
is concerned that people may believe the goal unreachable 
because it will be thought to require that inanimate 
mechanisms “think.” Marill refers to the question-answer- 
ing system called “Baseball” to forestall that misconcep- 
tion (Green et al., 1961). 

In his discussion of question-answering systems, Marill 
defines the primary concepts: the corpus, the question, 
and the answer. The corpus consists of a set of quantifica- 
tional schemata and their attendant predicate definitions, 
one definition for each predicate in the corpus. Thus 
Marill immediately seizes upon a predicate calculus as 
the formalism for representation of the information in 
the body of knowledge. There are two kinds of questions: 
questions satisfied by yes-no answers, and questions that 
require sentence answers. 

Answers, therefore, are also of two types: yes-no an- 



swers and sentence answers. The answer to a yes-no ques- 
tion is “yes” if the question can be deduced from the cor- 
pus — if there exists a deduction that has the sentences of 
the corpus as premises and the question (in statement 
form) as the last line. The answer is “no” if the negation of 
the question can be deduced from the corpus. There is no 
answer (or the answer is, “I don’t know,”) if neither the 
question nor its negation can be deduced from the corpus. 
A sentence qualifies as a sentence answer if the sentence 
can be deduced from the corpus and if the schema of the 
sentence is the same as the schema of the question when 
the question is in statement form. There can be only one 
yes-no answer, but there can be any number of sentence 

Marill’s treatment of semantic nets is an extension and 
formalization of the discussion of relational networks 
given in Part I. Marill takes the view that the proper 
structural analysis of a sentence is given by the quantifica- 
tional schema of that sentence, as understood in symbolic 
logic. To this, he adds the view that the meaning of a 
one-place predicate is identified, ultimately, with the set 
of objects of which the predicate is true, and the mean- 
ing of a two-place predicate is identified with the set of 
all ordered pairs of objects of which the predicate is true, 
and so forth. Finally, he adds the notion that the meaning 
of an object is identified with, first, the set of one-place 
predicates that are true of the object, and, second, the 
set of two-place predicates that are true of it and some- 
thing else, and so forth. 

The sets (of predicates and objects) with which mean- 
ings are identified are extremely large. The machine can- 
not be required to prove its “understanding” of a mean- 
ing by producing all the members of the sets. Marill 



maintains that it is enough for the machine to produce 
a convincingly large sample. 

The usual representation of a quantificational schema 
has the form of a string of symbols. Marill views the 
semantic net as an alternative notation, equivalent to the 
string notation, but more promising for computer ex- 
ploitation. To demonstrate the relation between semantic 
nets and string-form schemata, Marill starts with three 
simple statements in string notation and transmutes them, 
by degrees, into diagrams in which lines tie together the 
several instances of a variable. 

The elements of a semantic net in Marill’s exposition 
are the following. 

1. Rectangles — which represent the truth functions 
corresponding to logical operators and their arguments. 
Each rectangle has one terminal for each argument. 

2. Diamonds — which represent quantifiers. There 
are two types, corresponding to “some” and “all.” 

3. A triangle with point down — which represents 
“sentence.” This triangle contains an “S.” 

4. A circle — which represents a predicate. The circle 
has as many terminals as there are places in the predicate. 

5. A triangle with point up — which represents an in- 

Marill gives six rules governing the combination of ele- 
ments and the formation of semantic nets. The rules are: 

1. Connecting a predicate to a sentence symbol forms 
a sentence. 

2. Inserting a negation rectangle between the sentence 
symbol and the predicate negates the sentence. 

3. To connect two sentences, divert their predicates to 



a two-place truth-function rectangle and connect it to a 
single sentence-symbol triangle. 

4. To add a quantifier to a sentence, insert it in the 
line between the sentence symbol and its nearest neighbor. 

5. To terminate an unterminated predicate, attach an 
individual object to the unused terminal, or connect that 
terminal to a quantifier that is already in the diagram of 
the sentence. 

6. To merge two or more networks into one: (step 
1) for each individual, overlay all the triangles that repre- 
sent the individual, retaining all the lines that are at- 
tached to any of the triangles; (step 2) associate all the 
occurrences of the same predicate by connecting them 
with “association lines.” 

In “Semantic Nets: An Informal Introduction,” Marill 
presents diagrams to illustrate the rules and to demon- 
strate the interpretation of moderately complex semantic 
nets. Marill’s diagrams and discussion make it clear that 
the semantic net is formally equivalent to the conven- 
tional representation of a system of statements in predi- 
cate calculus. Marill believes that semantic nets afford a 
promising path into computer representation and process- 
ing of complex systems of relations. 



Studies of Computer 
Techniques and Procedures 

THE FOLLOWING EIGHT STUDIES are concerned with early 
steps along the course from present digital-computer sys- 
tem to future procognitive systems. Let us consider the 
studies in the order of their locations along that course. 
The first ones were intended merely to make it convenient 
to carry out some of the functions that are required in 
research on library and procognitive problems or in the 
efficient use of large collections of documents. The last 
ones in the sequence were intended to explore functions 
that we think will actually be involved in future procogni- 
tive systems. 




One of the first needs of the study was a computer pro- 
gram to facilitate the programming and use of the labora- 
tory’s digital computer. The computer is a small but ex- 
cellent machine, Digital Equipment Corporation PDP-1, 
specialized to facilitate interaction with users who work 
“on line.” The PDP-1 has an oscilloscope display with 
light pen, several electric typewriters, a set of program- 
mable relays, an analogue-to-digital converter, and an as- 
sortment of switches and buttons. Its primary memory is 
small (8196 eighteen-bit words), but it is capable of 
transferring information rapidly between the primary 
memory and a secondary drum memory that holds about 
90,000 eighteen-bit words. Associated with the computer 
are two magnetic-tape units. With the computer, it is 
possible to implement, in a preliminary and schematic 
way, several of the functions that were described in Part I 
as functions desirable in a procognitive system. 

One encounters two main difficulties in trying to do, 
with the PDP-1 computer, research oriented toward a 
future period in which information-processing machines 
will have more advanced capabilities. First, although the 
machine is reasonably fast (5-microsecond memory 
cycle), and although its secondary and tertiary memories 
make it possible to work with significantly large bodies 
of information, the machine is not capable of performing 
deep and complicated operations on really large bodies 
of text with the speed that would be desired in an opera- 
tional system. The result is that it may take 30 seconds 
or a minute to get something that one would like to have 



almost instantly, that the display flickers on the oscillo- 
scope screen, and so forth. Second, there is not available, 
at present, on any machine, either a programming lan- 
guage or a man-machine interaction (user-oriented) lan- 
guage that makes it easy to do, or possible to do rapidly, 
many of the things envisioned in the discussion of procog- 
nitive systems in Part I of this report. Our approach, dur- 
ing the study, was simply to put up with, and make allow- 
ances for, the shortcomings of the hardware system. It 
was easy to do that in informal experiments conducted 
by members of the research group; it was not realistic, 
however, to hope that all the observers of demonstrations 
would make the necessary allowances, and the shortcom- 
ings of the equipment (compared to what we expect to 
have in two decades) effectively precluded formal ex- 
perimentation. Nevertheless, the equipment situation was 
at least tolerable, in terms of absolute assessment, and it 
seemed superb when we compared our man-computer 
interaction situation with any but two or three of all the 
others with which we were acquainted. 

The unavailability of highly developed languages — 
and, of course, the interpreter and compiler programs 
that would be required to make them useful — was, how- 
ever, a seriously inhibiting factor. During the period of 
our study, two good programming-language systems came 
into being: DECAL, which is a quite elegant and power- 
ful language and compiler of the ALGOL type, suitable 
for a small computer, and MACRO, which is an ingen- 
ious language and assembler system that incorporates 
several of the features that DECAL lacks and lacks sev- 
eral of the features that DECAL incorporates. However, 
neither DECAL nor MACRO was designed for on-line 



programming, and neither was designed particularly to 
handle the problems that seem likely to present themselves 
to users of procognitive systems. 

Those considerations led to the effort, which was never 
quite completed, to develop a composite programmer- 
oriented and user-oriented system to facilitate our re- 
search in man-machine interaction. The system is called 
“Exec,” which stands, of course, for “executive program” 
and thus expresses something about the mode of opera- 
tion: statements of the input language, coming into the 
computer, are examined by the executive program, and, 
depending upon whether or not they fall into the class re- 
quiring interpretation, are either interpreted and executed 
with the aid of a set of subroutines associated with the 
executive program or simply executed as machine instruc- 

Exec was written originally in the symbolic language 
called “FRAP” and had, as its main function, simplifica- 
tion of the preparation of programs and subroutines to be 
translated and assembled by FRAP. When DECAL be- 
came available, Exec was rewritten in DECAL, and 
slight modifications were made to facilitate the use of 
Exec in the preparation of programs and subroutines to 
be translated and compiled by DECAL. The original in- 
tention — to develop Exec to the point at which it could 
Operate as an on-line language as well as an adjunct to 
a programming language — was not accomplished. 

One of the main themes in the work on Exec was to 
simplify and regularize the “calling” and “returning” of 
subroutines. In most computer-programming systems, and 
especially in the computer-programming systems that will 
be required in the implementation of plans of the kind 
described in Part I, a computer program is a complex 



arrangement of parts. The highest echelon of the struc- 
ture does little more than represent the chapter headings 
of the general plan. The actual work — the detailed 
processing of data — is handled by subprograms or “sub- 
routines” that break the task down into successively sim- 
pler subpackages at successively lower levels of detail 
until, finally, there is nothing left for the lowest-echelon 
subprograms to do but to perform simple, explicitly de- 
fined operations upon the few codes or numbers that are 
supplied to them. In this process of successive delegation 
of responsibility, the transactions that appear repeatedly 
are the “calling” of a lower-echelon subroutine by a 
higher-echelon subroutine and the “returning” of control 
from the lower-echelon subroutine to the higher-echelon 
subroutine. Calling usually includes transmission of in- 
structions, and also the designation of the arguments upon 
which the lower-echelon subroutine must operate, from 
the calling subroutine to the called subroutine. The trans- 
mission of information down the line we shall call “brief- 
ing.” Transmission of results up the line we shall call 

In the conventional way of handling calling and re- 
turning, a subroutine eventually returns control to the 
subroutine that called it, but it may first call one or more 
lower-echelon subroutines. 

Fortunately or unfortunately, depending upon one’s 
point of view, there are many different ways in which 
subroutines can be called and briefed and in which they 
can return control and do their debriefing. As indicated, 
one of the main purposes of Exec is to simplify and regu- 
larize this whole process. 

In order to simplify the process of calling and return- 
ing, Exec is interposed between the calling subroutine and 



the called subroutine at the time of calling and between 
the called (and now returning) subroutine and the call- 
ing (and now receiving) subroutine at the time of re- 
turning. With each subroutine is associated a compact 
code, that provides Exec with a description of the needs 
of the subroutine. Exec can therefore handle in a system- 
atic, centralized manner several of the functions that 
would otherwise have to be handled by each subroutine. 
In taking a burden off the routines, Exec takes a burden 
off the programmers who prepare them. 

The arrangements in Exec for calling and returning 
are set up in such a way that the chain of subroutine calls 
can be recursive. That is to say, it is possible for sub- 
routine A to call subroutine A, or for subroutine A to 
call a subroutine B which, directly or through one of its 
minions, called subroutine A. To permit recursive opera- 
tion, one must handle “temporary storage” — storage of 
the scratch-pad jottings made by each subroutine during 
its operation — in such a way that results written by B, 
for example, do not destroy results that A calculated be- 
fore calling B and will need to use after B returns con- 

The functions just described have been implemented 
in a few programming systems — notably in the systems 
called IPL and LISP. In IPL and LISP, however, one 
either works wholly within the system or does not use the 
system at all. Exec extends the basic programming lan- 
guage (DECAL) and provides the new capabilities and 
‘conveniences within the structure of DECAL. 

In implementing the handling of subroutines, we made 
use of the technique of the “pushdown list.” A pushdown 
list (or “stack”) is an arrangement for storing informa- 
tion that resembles the spring-supported tray on which 



plates are stored in restaurants. If one puts a plate (com- 
puter word) onto the top of the stack, it pushes all the 
others down, and if one then takes the plate (word) off 
the top, the others pop up again. Exec, itself, employs 
one pushdown list. Another pushdown list is available to 
the programmer or user through simple commands. He 
has only to give the name of the entity to be stored into 
the pushdown list or returned from it and to say whether 
he wants to push it down or pop it up. In the process of 
handling a call to a subroutine or a return from a sub- 
routine, Exec examines the subroutine’s heading code, 
determines whether or not, and how, to fulfill each of a 
number of functions, and then carries out those required. 
These functions include protecting contents of certain 
special registers of the processor against destruction dur- 
ing the running of the subroutine, finding the arguments 
needed by the subroutine and displaying them for its use, 
accepting the results obtained by the subroutine and com- 
municating them to the calling routine, and protecting the 
contents of various temporary storage registers and “flag” 
registers against modification by the called subroutine. 
Exec protects information by putting it into the pushdown 

A second general purpose of Exec is to provide the 
advantages of generality and the advantages of specificity, 
both at the same time and within the same system. If a 
computer program is written in such a way as to make it 
useful in a particular situation — for example, to make 
it operate on words and not sentences, and on the con- 
tents of Table 3 instead of the contents of Table 4 — 
then a new program must be written every time the spe- 
cifics of the problem change. On the other hand, if the 
program is written so that for example, it alphabetizes or 



orders any kind of strings of alphanumeric text in any file 
or table, then, when the programmer starts to use it on 
the words listed in Table 3, he has to communicate to it, 
or have the routine that calls it communicate to it, that 
it should operate on words and on Table 3, and that it 
should alphabetize according to a specified alphabet. 

In Exec, an effort is made to accommodate very gen- 
eral subroutines and to make it maximally convenient to 
communicate to them the information required to pre- 
pare them for specific applications. This is done by setting 
up and maintaining a description of the prevailing context 
of operation. In the terms of the example, his description 
may contain a specification that the currently prevailing 
string class is the class of “words.” It contains the specifi- 
cation that the x, in any subroutine prepared to operate 
upon “Table x,” should be interpreted as 3. The sub- 
routine, therefore, automatically performs on Table 3 the 
operation intended by the programmer, despite the fact 
that the programmer was thinking in terms of abstrac- 
tions and not in terms of the particular present task. 
(Exec makes it possible to write subroutines in terms of 
table variables x, y, and z simultaneously. A sequence of 
sentences can, for example, be taken out of Table x, and 
the odd-numbered ones can be stored in Table y and the 
even-numbered ones in Table z. The values of x, y, and z 
can be set later to 17, 4, and 11, respectively.) 

With arrangements of the kind just suggested, Exec 
makes it possible to use a subroutine that contains the ex- 
pression, “next string,” for example, to operate on the 
next character, or the next word, or the next sentence, or 
the next paragraph, or the next section, or the next chap- 
ter, and so forth. Exec keeps track of three “string classes” 
simultaneously, class u, class v, and class w. At any time, 



the programmer can set any one of the string classes to 
any one of seven levels. He can write, for example, “sscu 
word,” meaning to set the string class u to have the value, 
word. From that time on, until the instruction is super- 
seded, all the subroutines that deal with the string class 
hierarchy wu will consider the u strings to be words. 

The procedure for designating tables is similar to that 
for designating strings. If the programmer would like to 
have programs written in terms of Tables x, y, and z 
operate on the contents of Table zones 2, 5, and 7, he 
writes “ntox 2, ntoy 5, ntoz 7.” * When Exec sees those 
instructions, it does more than merely substitute 2 for x, 
5 for y, and 7 for z. It finds the descriptions, in its file of 
table descriptions, that characterize the three numbered 
tables, and it substitutes these descriptions for the pre- 
previously prevailing descriptions of Tables x, y, and z, 
respectively. When a subroutine operates on the contents 
of the table, it examines the table’s description and con- 
trols its processing accordingly. 

This makes it possible to accommodate diverse for- 
mats and conventions. Inasmuch as the adjustments are 
made “interpretively” during the running of the program, 
the user can change his mind and reprocess something in 
a slightly different way without having to go through ex- 
tensive revision and recompilation of his programs. This 
is an advantage that the present technique has over the 
technique, based on the “communication pool,” that has 
been developed in connection with the programming of 
very large computer systems — systems programmed by 
teams so large as to discourage the effort to enforce the 

* The italics are introduced here in the hope of bringing out the 
mnemonic significance of the code: “move n to x, and let n be 2.” 
The programmer’s typewriter does not have italic type. 



use of a single, standard set of conventions and formats. 
The part of Exec that we have been describing is approxi- 
mately an interpretive communication pool. 

The third set of functions with which Exec is concerned 
has to do with the display of alphanumeric information 
on typewriters and on the oscilloscope screen. Although 
these are very simple functions, they involve enough de- 
tailed programming to be a nuisance unless they are 
handled in a systematic way. Exec makes it convenient to 
separate specification of the information to be displayed 
from specification of the equipment through which it is 
to be displayed. It uses standard programs to handle 
strings that are long enough to be considered messages or 
texts, but it provides special arrangements to facilitate 
preparation of labels, headings, and the like. For example, 
the programmer can call for the typing of any particular 
character x on whatever typewriter is currently specified 
to be typewriter b simply by writing “typb x.” If the pro- 
grammer wants the character to appear upon the screen of 
oscilloscope a (there is only one oscilloscope now, but 
we hope to have more), he writes “scpa x.” With the aid 
of Exec, the programmer can define in equally short in- 
structions the size of the print, the vertical position at 
which the text should begin, and other parameters of the 
visual display. Exec’s arrangements for displaying capital 
and lower-case letters on the oscilloscope are primitive, 
but it is a step in the right direction to have both capital 
and lower-case letters. At present, a “lower-case” letter 
is simply a small capital letter. We settled for that stop- 
gap solution only in the interest of economy. 

The fourth and final set of functions handled by Exec 
has to do with display, by the computer, of what the com- 
puter is doing. One technique developed for this purpose, 



a technique called “Introspection,” will be described in a 
later section because it was developed as a separate 
project. The arrangements described here are integral to 

One of the main causes of difficulty in man-computer 
interaction is that the computer does not give the man any 
good clues about what it is doing until it completes a 
segment of processing and spews forth the results. When 
the computer is running, its lights flash so fast that they 
are scarcely interpretable. It seems important to provide a 
way of having the computer give a running account of 
its processing. 

A part of Exec called the “Reporting Subsection” — 
an optional part — is brought into play each time a sub- 
routine is called and each time a subroutine returns con- 
trol to its caller. When the reporting subsection is brought 
into action, it examines the list of things that it should 
do. This list can be changed while Exec and other pro- 
grams are running. Ordinarily, the first thing on the list 
is to give the address, and, if it is available in the direc- 
tory, also the name, of the subroutine that is being called 
or that is returning control to its caller. Since the sub- 
routines operate very rapidly, the names and addresses 
would appear to be presented simultaneously, one on top 
of another, if they were shown in a fixed location on the 
screen. Therefore they are displayed in a format corre- 
sponding to that of a conventional outline. If a chain of 
subroutines is called, each one operating at a level just 
lower than its caller, the names and addresses appear on 
successive lines of the display with increasing indentation. 
Then, as the subroutines return control, each to its caller, 
the names and addresses are displayed again in such a way 
as to redisplay the outline pattern from bottom to top. 



To the display just described can be added, at the op- 
tion of the operator, a display of the contents of the 
active registers of the computer. In addition, the operator 
may see the contents of whatever parts of the computer’s 
memory he wants to examine. He designates the various 
parts of memory to Exec by typing on the typewriter 
while the program is running. He can change his pre- 
scription at will. He may, for example, ask to see the 
contents of Table x, the contents of Table 3, and the pro- 
gram of the subroutine itself. The current version of Exec 
allows him to specify nine different sectors of memory, 
either symbolically or in terms of absolute addresses. 
When he indicates that he wants to see “the subroutine,” 
Exec interprets “the subroutine” to mean the particular 
subroutine that is being called, or that is returning con- 
trol to its caller. That will, of course, be one subroutine 
at one moment and another at another moment. When 
the operator wishes to examine a table or a program in 
detail, he touches the space bar of the typewriter. That 
causes the system to pause in its progression through the 
sequence of things to be displayed, and to hold the cur- 
rent display until the operator releases it by touching the 
tab key. 

The Reporting Subsection provides a few additional 
conveniences — minor ones introduced from time to time 
on an ad hoc basis — but the foregoing will suffice to 
give an idea of the existing arrangement. Let us mention 
a few of the steps not yet accomplished, however. It 
- seems worth while to connect to the Reporting Subsection 
the “Introspection” programs that will be described later. 
It is necessary to complete the arrangements that associ- 
ate the tables (x, y, z, 1, 2,3, - - -) that reside in the 
primary memory to corresponding structures in secondary 



and tertiary memory and, in the manner of the Atlas 
computer system, to arrange it so that information struc- 
tures are automatically shifted up through the memory 
hierarchy whenever they are addressed. It is necessary, 
also, to expand the mechanism that associates symbols 
with machine addresses. That mechanism is only a sim- 
ple table-searching system, but it is inherently capable of 
effecting the translation required to make conveniently 
readable the reports of “what is currently going on in 
the computer.” 

We found it useful to distinguish, in Exec, between 
“intrinsic” and “extrinsic” subroutines. Exec is highly 
“subroutinized” and has the partly hierarchical, partly 
recursive, structure that we have described as essential 
for procognitive systems. Each subfunction of Exec that 
appears to have any likelihood of proving useful in fu- 
ture applications is separated out and set into the form of 
a subroutine. 

In the process of writing subroutines to handle sub- 
stantive problems — subroutines that made use of Exec 
but were not at first intended to be part of the Exec system 
— we encountered repeatedly several sets or clusters of 
functions. By associating with Exec the subroutines pre- 
pared to handle those functions, we were able to build 
up a system of considerable convenience and power. The 
part of the system not intrinsic to Exec was too extensive 
to be held in primary memory at all times. However, it 
was clearly desirable to bring parts of it into primary 
memory — coherent clusters of it corresponding to major 
functions — whenever required in the execution of a 

The easiest way to accomplish dynamic storage and 
transfer of subroutines, and to handle the associated 



bookkeeping, was to take care of it automatically through 
Exec’s ability to examine calling sequences and subrou- 
tine headings. We did not make much progress toward 
that end during the course of the study. However, we did 
work with the problem enough to see the great con- 
venience and power that reside in a coherent structure of 
computer subroutines and a largely automatic arrange- 
ment for calling them and transferring information among 
them. Evidently, the more sophisticated the arrangements, 
the larger the fraction of the subroutines that will be in- 
trinsic to the arrangements. We visualize a system in a 
continual process of development, with a set of intrinsic 
subroutines, a set of extrinsic subroutines, and a continual 
flow from the extrinsic set to the intrinsic set as more 
and more functions are brought within the scope and 
capability of the system. 


“On-Line Man-Computer Communication” by Lick- 
lider and Clark (1962) discusses several problems in, 
and several steps toward the improvement of, interaction 
of men and computers. These include problems and de- 
velopments in the use of computers as aids in teaching 
and in learning and as a basis for group cooperation in 
the planning and design of buildings. The part of the 
paper that stemmed from the present study was the de- 
velopment of a pair of programs, referred to earlier as 
“Introspection,” that are closely connected with the last- 
described major function of Exec. 

The two programs of “Introspection” were designed 
to demonstrate that, although present-day computers are 
opaque and inscrutable, of all the complex organisms and 



systems in the world, computers are, in principle, the most 
capable of revealing the intricacies of their internal proc- 
esses. We consider this to be an important problem call- 
ing for much research. Our two programs constitute only 
exploratory steps. 

The two parts of Introspection are “Program Graph” 
and “Memory Course.” Program Graph displays, in the 
form of a graph relating that quantity to time, the con- 
tents of any specified register or registers of the computer. 
Memory Course displays the progression of control 
from one memory register to the next during the operation 
of a program. With the aid of these two programs, the 
operator can see what is happening, as it happens, within 
the processor and the memory of the computer. These 
programs give him at once both a global view and a con- 
siderable amount of detail. They let him see relations 
among parts of the over-all picture. No longer is he con- 
strained, as he has been with conventional procedures, 
to peek at the contents of one register at a time, and to 
build up the over-all picture from myriad examinations 
of microscopic details. 

To provide a rough impression of the operation of 
Program Graph, it may suffice to describe how it oper- 
ates when it is set to display the contents of the register of 
the computer that is called the “program counter.” The 
program counter contains the address of the memory 
register that contains the instruction that is being exe- 
cuted. In the absence of “branching” or “jumping,” con- 
trol proceeds from one register to the next, and the con- 
tents of the program counter increase by one, each time 
an instruction is executed. When a “branch” or “jump” 
occurs, the number in the program counter changes by 
some integral quantity different from one, and often the 



increment or decrement is rather large. Program Graph 
plots the graph relating the number in the program 
counter to the time. The display presents about a thou- 
sand individual quantities simultaneously to view. From 
the graph, it is easy to recognize the upward-sloping line 
segments that correspond to nonbranching, nonjumping 
stretches of program. When the program “loops,” as it 
often does, branching backward and repeating a sequence 
of instructions over and over, the display shows a saw- 
toothed waveform. When the program calls a subroutine, 
the jump to the subroutine, the loops within the subrou- 
tine, and the return from the subroutine are all clearly 

When Program Graph is used to display the contents 
of the accumulator, the input-output register, or one of 
the memory registers, the interpretation of the graph is, 
of course, quite different. In general, however, its main 
value lies in its presentation of a large quantity of in- 
formation in such a way that relations among parts are 
easy to perceive. 

The other Introspection program, Memory Course, 
displays only the course through memory followed by 
the program under study. It shows that course as a suc- 
cession of circles connected by a heavy line against a 
gridlike background representing the primary memory of 
the computer. The grid upon which the display of Mem- 
ory Course is shown consists of 4096 dots, arranged in 

64 squares of 64 dots each, and representing one bank of 
_ memory. A register is represented by a very fine light dot 
if the instruction and the address it contains are both 
zero. The dot is a little heavier if the instruction is 
not zero. The dot is a little heavier still if the address 
is not zero. If both the instruction and the address are 



not zero, the dot is heavy. From the grid, therefore, the 
user can see which parts of memory are occupied and 
which are not. In addition, after he has gotten used to 
the display, he can make out which parts of memory 
are used to store programs, and which parts are used to 
store data. 

When Memory Course is used to display the “trajec- 
tory” through memory followed by an object program, 
the object program itself is not run in the usual way. In- 
stead, the object program is operated by Memory Course, 
which “traces” the progress of the object program and dis- 
plays it on the oscilloscope screen. Each time an instruc- 
tion is executed, a circle is drawn around the dot that 
corresponds to the location of the instruction in the com- 
puter memory. When a program “loop” is traced, the line 
is set over slightly to one side of the circles it has been 
connecting. That keeps it from retracing its path back- 
wards and helps it represents the cyclic nature of the 

Memory Course represents loops, as just suggested, by 
tracing out a closed course. When the program transfers 
control to a subroutine, a line jumps out from the dot that 
corresponds to the call and leads to the dot that corre- 
sponds to the beginning of the subroutine. Thus, Memory 
Course provides a simple, maplike representation of the 
program structure. One can see where the various sub- 
routines are, how long they operate, when they receive 
their calls, and when they return control to their callers. 
If an error occurs, either in the computer or in the pro- 
gram, control is very likely to be transferred to an inap- 
propriate location. If the user knows the structure of his 
program, either from having programmed it or from 
experience Operating it, he sees that something unex- 



pected has happened. He then looks back to the begin- 
ning of the unexpected line and determines precisely the 
location of the register within which the error originated. 
Having done that, he typically reruns the program, fol- 
lowing its course very carefully as it approaches the criti- 
cal point. If the error recurs, he reruns the program once 
more, this time stopping it at various points ahead of 
the critical one and using other means to examine the 
instructions, addresses, and data associated with those 


The project to be described next was aimed, like Exec, 
at increasing the convenience and effectiveness with which 
the computer could be used in the study of library and 
procognitive problems. This project, however, had a 
much sharper focus than Exec. Its aim was simply to 
implement the operation called “file inversion.” 

A direct file is ordered with respect to its “items,” and 
usually several terms are associated with each item. An 
inverse file is ordered with respect to its “terms,” with 
several items usually associated with each term. Obvi- 
ously, both the direct file and the inverse file are aspects 
of a more general structure consisting of items, terms, 
and associations between items and terms. 

The “File Inverter” is a computer program, written in 
DECAL by Grignetti (19635), that accepts a direct 
file and produces an inverse file. Since there is no dif- 
ference in abstract format between a direct file and an 
inverse file, the program produces a direct file if it is 
presented with an inverse one. 

The file-inverting program includes a subprogram that 



alphabetizes the entries. If it is used to invert a file con- 
sisting of terms associated with alphabetized items, it 
yields a file of items associated with alphabetized terms. 
If the items consist of the bibliographic citations of 
documents, and if the terms are the key words of the 
titles of the documents, then the result obtained by ap- 
plying the file-inverting program is a kind of “permuted 
title index.” Grignetti’s program includes a subprogram 
that facilitates the selection of key words from titles (or 
from abstracts or from texts). The subprogram selects 
from a string of words all those that do not appear upon 
a list of words to be excluded. The list of words to be 
excluded ordinarily contains the “function” words and, 
also, words that have been found not to discriminate. 


Using parts of the file-inverting program, Grignetti 
(19635) prepared a program that automates some of the 
functions involved in using an ordinary card index. The 
kind of card index toward which the program is oriented 
is not precisely the kind used in most libraries. It differs 
mainly in assuming that each card will contain a series 
of descriptive terms. Such card indexes are found more 
frequently in documentation centers that specialize in 
laboratory technical reports and reprints than in libraries 
of books and serials. 

The “Automated Card Catalogue” is a DECAL pro- 
gram for use in exploration of card catalogue problems. 
The user sits at the computer typewriter and presents his 
retrieval prescription to the computer in the form of a 
Boolean function of the terms in which he is interested. 
A person interested in non-digital simulations of neural 



processes, particularly including studies made under the 
heading, “perceptron,” but also other studies in the field 
of artificial intelligence, might type: 

(artificial inteligence or perceptron or 

neural simulation) and not digital 

Using the terms of the Boolean function as retrieval 
terms, the program searches a magnetic tape containing 
the “card” file. Whenever it finds one of the terms, it 
looks further within the entry to determine whether or 
not the function is satisfied. If the function is satisfied, 
the program displays the entire contents of the “card” 
on the oscilloscope screen for examination by the user. 

Grignetti’s program makes it convenient for the user 
to correct his retrieval prescription, to reinitiate a search, 
to find out just where he stands at any point in his study, 
and to save “cards” for future reference. The program 
“knows” the rules for regular pluralization and considers 
the search for a term to be satisfied if either the singular 
or a calculated regular plural or a given irregular plural 
of the term is found. In addition, the program works 
with a simplified system of spelling, as well as with 
literal spelling, and is therefore often able to find the 
desired term on a “card” even when the term is misspelled 
in the prescription. In such an instance, it displays, for 

Do you mean “intelligence”? 

The user then types y for “yes” or n for “no.” The pro- 
' gram remembers this answer and does not bother the 
user again with the same question. That may be con- 
venient when the user is dealing with names he does not 
know very well, but it leads to complications that will 
have to be settled through further programming. Prob- 



ably it will be better to correct the prescription than to 
perpetuate the indiscrimination. 


The two programs described in the preceding sections 
are related to, and are intended for incorporation into, 
a system to facilitate the retrieval and study of docu- 
ments. The “study” part of the over-all system is described 
in a report by Bobrow, Kain, Raphael, and Licklider 

The study system, called “Symbiont” because we hope 
to develop it into a truly symbiotic partner of the student, 
displays information to the student via the typewriter or 
the display screen. It is intended as an exploratory tool, 
for use mainly by students who are at the same time 
experimenters, and it does not yet have the perfection or 
polish required for realistic demonstration or practical 
application. However, it does make available, in a single, 
integrated package, several functions that prove quite use- 
ful to a student who wants to examine a set of technical 
documents, take notes on their contents, compare or 
combine graphs found in different papers, and so forth. 

Among the functions provided by Symbiont are the 

1. Present for examination a document specified by 

any sufficiently prescriptive segment of its bibliographic 

2. Turn pages, forward or backward, in response to 
the pressing of a key. 

3. Permit designation of a passage (segment of text) 



by pointing to the beginning and then the end with a 
light pen. 

4. Accept labels from the typewriter and associate 
them with passages of text. 

5. Record as a note, and preserve for later inspec- 
tion, any designated passage. 

6. Append bibliographic citations to extracted pas- 

7. Accept retrieval prescriptions from the typewriter. 

8. Accept from the typewriter coded versions of 
specifications of such operating characteristics as, “Con- 
sider a neighborhood to be five consecutive lines of text,” 
or “Consider a search to be satisfied when any two of 
the three elements of the search have been satisfied.” 

9. Carry out retrieval searches and display passages 
in which the retrieval prescriptions are satisfied. 

10. Compose graphs from tabulated data and present 
the graphs, against labeled coordinate grids, on the oscil- 
loscope screen. 

11. Set two graphs side by side to facilitate compari- 

12. Expand or compress the scales of graphs, under 
control from the light pen. 

13. Change the number of grid lines or the calibration 
numbers associated with the lines, or both together, and 
recalculate and redisplay the calibration numbers when 
grid lines are added or deleted. 

The search routines used in finding desired passages of 
text operate with three sets of retrieval terms. The user 
specifies the terms of each set initially through the type- 
writer. All the terms of a subset are considered equivalent 
during the search, and the search is satisfied insofar as 



that subset is concerned if any one of the terms is en- 
countered in the text. The user can specify whether he 
wants to find a passage in which at least one of the terms 
of one of the sets occurs, or a passage in which at least 
one of the terms of each of two of the sets occurs, and 
so forth. Even though this implementation is primitive, 
it is evident from preliminary experiments with Symbiont 
that automation of the function of searching for “ideas” 
will be a very powerful aid in technical study. Machine 
aid in manipulating graphs will also be very helpful. 


Most of the information-retrieval systems that have ac- 
tually been developed, and even most of those that have 
been subjected to intensive research, retrieve unitary 
elements of information, such as documents, paragraphs, 
or sentences. A basic point in Marill’s (1963) paper, 
discussed in Chapter 8, is that for many purposes the 
retrieval of a unitary part of the corpus is inadequate, and 
that what often is needed is an answer to a question that 
may have to be derived through deduction from elements 
of information scattered throughout the corpus. The as- 
sociative chaining technique to be described briefly in 
this section is a step in Marill’s direction. It does not go as 
far as the techniques described in the final two sections, 
but it does go beyond the single, unitary element of the 
corpus to explore “chains” of relation between one ele- 
ment of the corpus and another. When the relation be- 
tween two items is direct, they are said to be connected 
by a first-order chain. When the relation between two 



items can be established only through the intermediary 
agency of a third item, the first two items are said to be 
connected by a chain of second order, and so forth. 

In a report on “Associative Chaining as an Information 
Retrieval Technique,” Clapp (1963) describes the idea 
of chaining as a general schema, then shows the corre- 
spondence between the chaining schema and certain 
schemata of graph theory, and finally discusses a pro- 
gram that traces chains of relevance through corpora 
consisting of files of sentences. 

Chaining, as a technique, is particularly simple and 
easy to discuss when it is separated from the problem of 
the nature of relevance. In Clapp’s work, the two things 
— the technique and the concept of relevance — are well 
separated. For purposes of simplicity and convenience, 
Clapp considers two sentences to be directly associated if 
they have one or more words in common. Thus, the sen- 
tences, “The cat is black,” and “Black is a color,” are 
directly associated. They have two words in common, “is” 
and “black.” There is no direct, first-order association 
between the first two of the following sentences, but only 
a second-order association through the third sentence: 
“The cat is black,” “Feline animals move gracefully,” “A 
cat is a feline animal.” It is obvious, even at the outset, 
that something has to be done to inhibit associations 
based on the common occurrence of frequently used verbs 
and function words. In Clapp’s approach, however, what- 
ever is done about that is a separate matter from the de- 
- velopment of the algorithm that traces out the chains. 

Clapp’s computer programs are divided into two sets. 
The first set of programs facilitates the preparation of a 
machine-processible file of information units, such as 
sentences, paragraphs, or documents. It then prepares, 



from the file, a series of concordances. Finally, with the 
aid of the concordances, it determines the set of all first- 
order associations. The second set of programs operates 
upon a retrieval prescription plus the set of first-order 
associations. The retrieval prescription is a set of words 
drawn from the vocabulary of the corpus. The first thing 
that the chaining algorithm does is to find all those ele- 
ments of the corpus that contain all the words of the 
prescription. This is what a “conventional” information- 
retrieval system would do. Then, however, the chaining 
algorithm goes on to trace higher-ordered chains through 
the corpus and to retrieve the information elements that 
are involved in higher-ordered chains up to some cutoff 
order specified by the operator. 

The program has been tested and demonstrated only 
with a corpus consisting of sentences. Except for minor 
considerations having to do with delimiters — the clues 
that mark stopping points such as ends of sentences or 
paragraphs — the chaining programs are not sensitive to 
the distinctions among sentence, paragraph, document, 
and so forth, and it is obvious that the chaining operation 
can be carried out on textual strings of any class. How- 
ever, pursuing the technique of chaining based on the 
common occurrence of words beyond a level of the sen- 
tence does not seem to offer much promise. It is evident 
that every book would be directly associated with almost 
every other book if the criterion were a word in com- 
mon, and it is equally evident that almost no book 
would be associated with any other book if the criterion 
were a verbatim paragraph in common. For the tech- 
nique of chaining, ordinary sentences seem to be ap- 
proximately the optimal length. 

Fortunately, the sets of descriptive terms used in co- 



ordinate-indexing systems are of approximately the same 
length as sentences. The notion of association based on 
inclusion of common terms is quite appropriate for them. 
It is in that domain that we think it most likely that the 
chaining technique, and the chaining algorithms de- 
veloped by Clapp, will find practical application. 

In his exploration of the relations between graph 
theory and associative chaining, Clapp developed the 
chaining schema in considerably more depth than is re- 
flected in this summary. For example, his development 
uses the number of parallel links as well as the order of 
the links in the chain of association. Some of his ideas 
(but not the algorithms thus far programmed) recognize 
gradations in the strength of association. That seems im- 
portant because, intuitively, one thinks of relevance as 
capable of variation in degree. 

The next step in the development of the concept of 
associative chaining, we think, should be an attempt to 
define the fundamental relatedness or relevance on which 
the “association” is based. Associative chaining has a 
natural connection with the relational networks de- 
scribed in Part I and with the semantic nets and question- 
answering systems studied by Marill and Black. The next 
step may, therefore, take the form of merging the chain- 
ing concept with the concepts underlying the relational 
and semantic nets and the question-answering systems. 


Marill’s (1963) short paper on question-answering 
systems, described earlier, initiated a series of studies that 
involved a meld of symbolic logic and computer pro- 
gramming. Most of these studies were carried out by 



Black, who described them in a series of memoranda and 
a report (1963). The memoranda and the report share 
with the corpora of the question-answering systems a 
tight, terse, logical quality that makes them attractive to 
the logician and difficult for the nonlogician to under- 
stand. Following is an effort to summarize, without gross 
distortion, two of the principal accomplishments of the 
work on question-answering systems in a freer and less 
formal exposition. One might justify this aim by quot- 
ing a paragraph from Black’s “Conclusions on QAS,” 
a memorandum dated November 13, 1963: 

A string of words cannot be rephrased without significant loss 
of facts or ideas relevant to some area. However, if we limit 
ourselves to certain areas, then the string of words can be 
rephrased without loss of facts or ideas relative to those areas. 

In the final paragraph of the same memorandum Black 
goes on to say: 

Before we can rephrase a string of words without significant 
loss, we must define our interests precisely. If we are inter- 
ested in everything, then we cannot rephrase the string at all. 

Let us say, therefore, that we are interested in assessing 
the possibility, and also the technical feasibility, of (1) 
representing large parts of the body of knowledge, as 
well as questions relating to the body of knowledge, in a 
formal language amenable to processing by a computer 
and (2) developing a system that will, by processing the 
questions and the stored corpus, deduce and display cor- 
rect answers. 

Black’s results attest to the possibility of doing those 
things. However, Black’s programs take a long time to 
determine the answers to fairly simple questions. That 
fact suggests that economic feasibility is dependent upon 



greatly increasing the processing efficiency of the ques- 
tion-answering system or the processing speed of the com- 
puter, or both. The prediction made in Part I, that it is 
unlikely that there will be great increases in speed in 
the same computers that have a greatly increased memory 
capacity, may not bear very heavily on this problem. It 
may be that we will use procedures that are fast, but not 
very deep, to retrieve parts of the corpus that are rich in 
statements germane to a particular question, and then 
turn to deeper and slower procedures for the derivation 
of the answer from the rich informational ore. 

The first of Black’s two contributions to be summarized, 
the memorandum, “Specific-Question-Answering Sys- 
tem,” February 8, 1963, describes Version III of a system 
written in the LISP language for the IBM 7090 com- 
puter (McCarthy et al., 1962; Berkeley and Bobrow, 
1964). In this system, the corpus consists of statements 
that are strings of ordinary words, symbols representing 
variables, and parentheses. The use of the ordinary words 
is highly constrained — so constrained that nothing can 
be said that could not be said equally well in the shorter, 
but less widely readable, notation seen in books on logic. 
The only variables are XJ, X2, X3, what, when, which, 
and how. The parentheses have the effect of forcing the 
system to consider as a unit the string within the paren- 

The “questions” asked of the Specific-Question-An- 
swering System may be either statements, in which case 
- they are confirmed or denied by the system, or ordinary 
questions containing the variables, XJ, X2, X3, what, 
and when, etc. The answer elicited by a “yes-no” ques- 
tion is “yes,” “no,” or “no answer.” The answer to any 



other question is a list of items that constitute a correct 
and reasonable reply, or “no answer.” 

The system seeks answers to questions by processing 
the questions and the corpus in a very straightforward, 
rigorous way. It looks through the corpus for a state- 
ment that is the same as the statement that constitutes the 
question (in the case of questions in statement form) or 
that can be transformed into the question by removing a 
“not.” If it finds a match, the answer is “yes.” If it finds 
a negated match, the answer is “no.” If it finds neither, it 
looks for a conditional statement in the corpus in which 
the consequent matches the question. If it finds such a 
statement in the corpus, it undertakes to determine an 
answer to the subsidiary question, whether or not the 
premise of the conditional statement is true. Proceeding 
in this way, it tries every possibility of deriving the ques- 
tion or its negation from the statements of the corpus. 

The procedure for processing of questions containing 
variables is a little more complex than the procedure just 
described. It is necessary, in seeking an answer to a 
question containing a variable, to keep track of all the 
individuals (people, objects, etc.) that can be values of 
the variable. The process amounts, approximately, to 
determining the list of individuals that meet all the con- 
ditions that are imposed upon the variable. 

The system is capable of answering not only simple, 
single-variable questions, but also multiple questions 
(conjunctions of simple questions), conditional questions 
(containing if . . . then . . .), and even questions con- 
taining the names of LISP computer programs. In the 
latter case, there is a rigid format that must be followed 
in giving the name of the program and its arguments. 



All the foregoing structures mentioned as acceptable 
question forms are also acceptable as forms for state- 
ments in the corpus. In the corpus, a program name may 
occur even in the antecedent of a conditional statement. 
We mention these things to indicate that the system has 
the capability of expressing complex relations and of de- 
riving answers to complex questions. 

To see approximately what the system does, let us 
consider a few oversimplified examples and one more 
complex example. Suppose, first, that the corpus con- 
sists merely of two statements: 


The question asked of the system, in statement form, is: 

To that question, the system says, simply: 

Suppose, for the second example, that the corpus con- 
sists of only one sentence: 


The question asked of the system and the foregoing rudi- 
mentary corpus is: 

The answer given by the system to that question is: 

But now suppose that a second statement is added to the 
corpus. The corpus now consists of the two statements: 




The question is still: 

The system is now able to determine an answer. It says: 

This example illustrates one of the basic notions under- 
lying Black’s work: It is possible, and it may be highly 
desirable, to put most of the “intelligence” of the system 
into its corpus and to let the processing program itself 
retain a high degree of simplicity and, if the term is ap- 
propriate, formal elegance. As will be seen, the same 
notion appears in “Ontogeny,” to be described later, even 
though Ontogeny is approximately the antithesis of 
Black’s system in respect of rigor and tightness of formal- 

The final example involves a corpus consisting of eight 
direct statements and three conditional statements. They 







The question asked of the system and this corpus is: 
The answer provided by the system is: 


This last example would be more impressive than it is 
if the corpus contained a large number of irrelevant state- 
ments in addition to the statements shown. The presence 
of irrelevant statements would increase the length of time 
required by the computer in answering the question, but 
the computer has the great advantage, in operations of 
this kind, that it does not tend to forget the relevant facts 
already found while it is examining the irrelevancies. For 
a human being, on the other hand, a problem of the 
present kind that is difficult but nevertheless within one’s 
scope of capability becomes entirely hopeless as soon as 
a large amount of irrelevant material is introduced. That 
fact, we believe, is significant in its bearing on the prob- 
lem men face in drawing answers from the body of knowl- 
edge that is now held in libraries and document rooms. 

The other paper of Black’s that we shall discuss here 
is “A Question-Answering System: QAS-5” (1963). 
This paper describes in detail the operation of a later- 
generation question-answering system, a descendant of 
the Specific-Question-Answering System that we have 
been discussing. The main advances made in the interim 
between the two papers were advances in the handling 
of quantification and advances achieved by formalizing 
the language approximately in a way suggested earlier by 
McCarthy (1959). The advance in quantification makes 



it possible for the system to deal with problems involving 
“some” and “all.” The formalization of the language 
makes it difficult for the uninitiated reader to understand 
what is being done, but it reveals the flaws and pitfalls to 
the veteran in a clearer way than the more readable lan- 
guage does, and, moreover, it suggests what to do to cor- 
rect or avoid them. We consider the formalization, there- 
fore, to be a step more in a right direction than in a wrong 
one — a step that must be taken in order to reach a posi- 
tion from which it will be possible to move forward to 
simultaneous readability and formal effectiveness. 

The mode of operation of QAS-5 is similar in basic 
principle, though somewhat deeper and more complex, 
to the mode of operation of the Specific-Question-Answer- 
ing System. Some of the flavor of the method is given by 
the following protocol: 

Step 1: The system finds the first match for question 1 
in statement 6. 

Step 2: The system forms the backward transform of 
question 1 and statement 6, giving a new condi- 
tional (7) — at (desk, y), at (y, country) — at 
(desk, country). 

Step 3: The system sets up the first antecedent of (7) 
as a new question (2) — at (desk, y). 

Step 4: The system finds the first match for question 2 
in statement 2. 

Step 5: The system forms the transform of question 2 
and statement 2, giving the answer to question 
2 — (1) at (desk, home). 

The latter would be read, as one might possibly guess, 
“IT am at my desk at home.” 
The problem to which Black’s QAS-5 report is wholly 



dedicated is a problem posed by McCarthy in 1958 and 
hitherto not solved by any nonhuman system. It is a well 
known problem in artificial-intelligence and heuristic-pro- 
gramming circles and is called the “airport problem.” 
The problem, stated here informally, is fairly simple: 

I am at my desk, at home. My car is in my garage, which is 
also at my home. I want to go to the airport. The airport is 
in the same county as my home. I can walk from any point 
that I would call “at my home” to any other point that I 
would also call “at my home,” because, of course, the dimen- 
sions of the area subsumed under “home” are not very great. 
I can drive from any point in my county to any other point in 
my county. What should I do? 

The answer to the question, or the solution to the 
problem, is said to be: 

I should go from my desk to my garage on foot and get my 
car, and I should then drive my car from the garage to the 

Having stated the problem and given the solution, we 
should perhaps repeat that, although the answer is obvi- 
ous to any adult human being who understands English, 
no one had succeeded in devising a wholly automatic 
system that would derive the answer (or a formal state- 
ment corresponding to the answer) from the description 
of the situation and the statement of the question (or 
formalizations of them). Black’s report gives a step-by- 
step account of the procedure used by QAS-5 in solving 
the problem. In addition, it displays, point by point, the 
minor differences between the notation suggested by 
McCarthy and the notation employed by Black. 

The conclusions that we draw from our experience 
with question-answering systems are summed up in the 
assertion that the achievement of Black’s program in 



solving McCarthy’s problem is simultaneously a signal 
advance in automated question answering and a common- 
place performance for a moderate intelligence. In greater 
detail, the conclusions are: 

1. Clear progress is being made in bringing logical de- 
duction within the scope of automation. 

2. In the process of programming a system to accom- 
plish a feat such as the one described, one begins to see 
how extremely deep and complex are the intellectual 
processes that one accepts as commonplace and unde- 
manding of intelligence when those processes are carried 
out by people. 

3. In the running of such programs, one begins to 
sense the magnitude of the gulf that separates a demon- 
stration of the type just described from an economically 
feasible operating system. As the problems become more 
complex, and as the corpus becomes larger, the amount of 
time required for processing goes up steeply. This is a 
discouraging counterpoise to the pattern of growth of the 
information-processing technology described in Part I. 

4. At the same time, one sees, even at this stage, many 
ways in which processing can be made more efficient, and 
one senses that there are — waiting to be discovered — 
ways of formulating the procedure that are much more 
powerful than the ways thus far employed. 

In short, it appears to us that the domain of question- 
answering systems is an intellectually deep and techno- 
logically demanding area for research and development. 
As suggested, there is an extremely long way to go before 
useful answers can be deduced from extensive informa- 
tion bases at reasonable cost. On the other hand, it may 
well be that, in this area, each basic conceptual advance 



will be a long stride toward the procognitive systems we 
envision for man’s future interaction with the fund of 


This final project was pursued intensively during the 
first year of the study, but, for reasons not related to its 
degree of promise, it lay dormant during the second year. 
Although the project does not appear to be worth con- 
tinuing in its present form, the following description may 
prove useful. 

The approach selected at the outset — to try to mirror 
in computer programs the ontogenetic development of 
the human ability to generate and understand language 
— was quite different from the approach, then more pop- 
ular, based upon syntactic analysis. The approach 
adopted paid more attention to semantics than to syntax. 
However, many of the investigators who earlier had con- 
centrated on syntactic analysis have directed their efforts 
toward semantic analysis, and what seemed to us at the 
outset to be an unpopulated field is rapidly becoming 
crowded. That fact, together with our sharpening aware- 
ness of the very great difficulty and even greater extent of 
the task, account for the negativeness of our thoughts 
about reactivating “Ontogeny.” 

At the beginning of the project, it seemed to us to be 
_ a good idea to start with “baby talk” and to try to re- 

capitulate, as closely as possible, the development of the 
human language process. Recognizing the importance of 
the “verbal community” in each individual’s development 



of language process, we set up a situation in which an 
operator at the typewriter played the role of the verbal 
community and, acting as stimulator, instructor, rein- 
forcer, umpire, and protector, presided over the “shaping 
up” of language behavior in the computer. 

It was necessary, of course, to provide the computer 
with basic structures and capabilities corresponding 
roughly to those that would be inherited by a human 
being. It was necessary also to set up some domain of 
discourse that would be potentially “meaningful” to the 
computer, as well as to the operator, and that would pro- 
_ vide an analogue to the “environment” in which human 
beings behave and with reference to which most of their 
language — that is not about themselves — is oriented. 

One part of the internal mechanism — of the system 
of computer programs — seems worth describing despite 
the fact that its nature does not in any direct way deter- 
mine the nature of the over-all system. This part of the 
program is concerned with the representation, in the 
computer memory, of the words and phrases communi- 
cated between the operator and the computer. In the in- 
put and output equipment, the words and phrases take 
the form of strings of characters or character codes. The 
codes are the so-called “concise” codes for alphanumeric 
characters employed in the PDP-1 computer. Each code 
is a pattern of six binary digits. 

Representation of words, phrases, and so forth, as 
strings of coded characters is inconvenient and uneco- 
nomical for many information-processing purposes. In a 
computing machine that has registers of fixed length, it 
is inconvenient to have words and phrases of variable 
length. We were at the outset not so much concerned 



with economy of representation as with convenience of 
processing, and we adopted an approach designed mainly 
for convenience. We represented each word, or phrase, 
or sentence, or string of any recognized class, by a 36- 
bit code. The code was made up of a 30-bit main code 
and 6 bits of auxiliary information, which included desig- 
nation of the class to which the string belonged. A 36-bit 
code can be stored conveniently in two consecutive regis- 
ters of the PDP-1 memory. 

The rule for representing incoming words in the com- 
puter memory was the following. If the word consisted of 
five characters or fewer, the concise-code representation 
(filled out with the six auxiliary bits and with “filler 
characters” if necessary to make it come to a total length 
of 36-bits) is the computer representation; if, on the 
other hand, the word contained more than five characters, 
then the computer representation consists of the concise 
codes of the first three characters, the six auxiliary bits, 
and, in addition, a 12-bit “hash code” calculated by a 
rather complicated procedure from the concise codes of 
the remaining characters. This representation is capable 
of discriminating among about 4000 different words with 
the same three leading characters. Because the calculation 
of the hash code is carried out by a procedure akin to 
the generation of “random numbers,” one cannot be en- 
tirely sure that two different words will not yield the same 
code. Nevertheless, he can make the probability of a “col- 
lision” as low as he likes by selecting a sufficiently long 
. representation. 

In the initial stages of the work, we were not much 
concerned about accidental confusion of one word with 
another. Children certainly confuse words. Indeed, we 



were attracted by the hypothesis that some of the con- 
fusions that arise in human communication and thinking 
are attributable to something like a hash-code process in 
neural representation. 

The agent that converts strings of text into hash codes 
is, of course, a computer program. First it divides a string 
of text into words and determines a code for each word. 
Then it combines the words into phrases, using punctua- 
tion as a guide, and determines a hash code for each 
phrase from the codes for the words within the phrase. 
Then it determines a hash code for each sentence from 
the codes for the phrases within the sentence, and so forth. 
Thus, for each word, for each phrase, for each sen- 
tence, . . . , there is a 36-bit representation. After the 
conversion to this internal code has been effected, and 
until a stage is reached at which it is necessary to gener- 
ate a response in the form of a string of alphanumeric 
characters, all the processing is carried out with the in- 
ternal 36-bit codes. 

It is easy to transform alphanumeric text into internal 
codes. To do that, it is necessary only to apply the trans- 
formation programs that calculate the codes. However, 
to transform in the other direction — to go from the in- 
ternal code representation to a string of alphanumeric 
characters — is another matter. Because information is 
sometimes lost in the forward transformation, it is not 
possible simply to calculate the reverse transformation. 
It is necessary to employ a “table-searching” procedure. 
However, it is certainly neither necessary nor desirable 
to store every string of characters received in order to 
have it ready to type as a response. If one is to respond 
in a natural way, he must be able to generate sequences 



of words that he has never received. Moreover, there are 
too many strings of words to consider storing them all 
in a computer memory. The procedure adopted, there- 
fore, is to associate with each internal word code, but 
not with the code for any string of any class other than 
word, its complete concise code, i.e., the string of con- 
cise codes corresponding to the characters of the word. 

The association is achieved in the following way. All 
the internal codes, for words, phrases, sentences, and so 
forth, are kept together — along with other information 
— ina table called the “Hash Table.” One of the entries 
in the Hash Table, for each word represented in that 
table, is the address of the register in the Vocabulary 
Table in which the corresponding concise-code represen- 
tation begins. That makes it possible, given the internal 
code corresponding to a word, to find the corresponding 
concise code and to have the word typed on the computer 

For those internally represented strings that are not 
merely words, there is still another table, called the “Sub- 
address Table.” The entry in the Hash Table that is associ- 
ated with a sentence the way the Vocabulary Table ad- 
dress is associated with a word, is the address of a register 
in the Subaddress Table. At that address in the Subad- 
dress Table, one finds the beginning of a list of “subad- 
dresses” that are addresses of registers back in the Hash 
Table. In those registers in the Hash Table are the en- 
tries for strings of the next lower class. (Since this ex- 
ample started with a sentence, they are entries for 
phrases.) With each hash code for a phrase is associated 
the address of another register in the Subaddress Table. 
Going back to the Subaddress Table with that address, one 



finds addresses of registers in the Hash Table. Finally, in 
the designated registers in the Hash Table are addresses 
of registers in the Vocabulary Table. In the Vocabulary 
Table, of course, are the concise codes of the words. 

The procedure just described is implemented by pro- 
gramming, so no effort of thought is involved after the 
program has been perfected. The program runs much 
more rapidly than the typewriter can type, and there is 
therefore no observable delay. With the system, one can 
start out with a 36-bit hash code and wind up with a long 
typewritten sentence. If the initial code is the code of a 
paragraph, indeed he winds up with a paragraph. We 
have checked the system to that level of operation. Obvi- 
ously, nothing stands in the way of representing an entire 
book with a 36-bit internal code. However, one cannot 
uniquely represent the individuals of any set of size ap- 
proaching 2” with hash codes n bits in length. Our selec- 
tion of 36-bit codes, and our compromise in the direction 
of readability by man as well as by machine, was con- 
ditioned by the fact that we were working with a “young” 
language mechanism that would not be expected to de- 
velop a very large vocabulary for some time. 

It is now doubtless evident that a description of com- 
puter programs in ordinary language encounters serious 
problems of exposition and endurance. We shall, there- 
fore, not describe the entire Ontogeny program in as 
great detail. Let us, nevertheless, explain how the system 
is designed to keep track of the properties of the various 
words and phrases and the entities and operations for 
which they stand. 

The repository for factual information, in Ontogeny, 
is a table called the “Property Table.” One of the entries 



in each section of the Hash Table is the address of a cor- 
responding section in the Property Table. For conven- 
ience, the Property Table records the corresponding Hash 
Table address and also the internal code of the string with 
which the properties are associated. The properties them- 
selves are represented by internal codes. When it is neces- 
sary to determine the meanings of the property codes, one 
has to find the codes in the Hash Table and go on from 
there in the way just described. 

The structure within which properties are represented 
in the Property Table is a simple hierarchy, an “outline.” 
The rules for listing properties are loose. In the main, they 
were made up as problems arose, and indeed a certain 
amount of care was taken not to create a sharp, formal, 
rigid system. Syntactic and semantic properties are mixed 
indiscriminately. In the basic system, there is not even 
any distinction between the symbol and the thing for 
which it stands. That is to say, under “table” we might 
record the property of being used mainly as a noun, the 
property of being used sometimes as a verb, and the 
property of usually being made of wood. The representa- 
tion of this last property may take the form: 


However, the system would be expected to function with- 
out great difficulty if, through happenstance, the arrange- 
ment were set up as: 




We did not reach the point at which programs actually 
operated with that kind of irregularity of format, but we 
did have search programs that examined the “next level 
down” if they did not find a satisfactory property on the 
level initially assigned. 

From the description thus far, it may be evident that 
the Property Table is, by nature, full of circular defini- 
tions. Everything is defined in terms of something else — 
except for a relatively few primitives that are associated 
with subroutines. One of the properties of “move,” for 
example, is that move is often used as a verb. Another is 
that, when it is so used, it is to be implemented by exe- 
cuting a subroutine that is capable of taking arguments 
that answer “what,” “by whom,” “from where,” and “to 
where.” Some of the properties of “pencil” refer to its 
capability of serving as an argument. A pencil is “mov- 
able,” “takable,” “bringable,” and so forth. 

We are now almost in a position to turn our attention 
to the procedure through which an incoming message is 
processed and responded to by the computer. One more 
part of the system must be described, however, before 
that can be done conveniently. This remaining part is 
the one that has to do with the “domain of discourse” 
mentioned earlier. 

The domain of discourse is a model room equipped 
with a few items of furniture. The room has a door that 



can be opened to various degrees, a window that can be 
opened or closed, a table that can occupy any otherwise 
unoccupied position within the room, and a chair sub- 
ject to the same constraint. There are a book and a pen- 
cil, to be manipulated, an active agent called “Comp,” 
and another active agent called “Oper.” The discourse in- 
volves Comp and Oper and is actually carried out by 
the computer and the operator. 

The room and its contents are represented in the 
computer memory, of course, and they are also repre- 
sented diagrammatically by simple line drawings on the 
screen of the oscilloscope. When the door is opened, the 
representation of the door in the computer memory 
changes, and the schematic door on the oscilloscope 
screen (a straight line with a little figure near one end 
representing the door knob) swings. 

The basic subroutines, corresponding to operations in 
the domain of discourse, are implementations of “move,” 
“go,” “carry, “bring,” “open, — close,” “put,” ‘ete. These 
subroutines, together with the subroutines that handle the 
encoding and decoding, the search for properties and the 
analysis of input messages, were all that we actually pre- 
pared and operated. The plan encompassed two addi- 
tional classes of subroutines. The first of these was to 
handle the addition, deletion and modification of proper- 
ties, under the control of input messages. The second was 
to handle the addition and modification of subroutines, 
again under the control of input messages. If we had 
. been able to carry through to some accomplishments in 
the first additional category, we should have been able to 
increase the verbal capability of the system, but only by 
adding to its knowledge — to its vocabulary and its fund 
of facts. If we had been able to move on into the second 

99 66 



additional category, we should have had within our grasp 
the capability of achieving almost unlimited restructuring 
and reorganization of the system. But we did not accom- 
plish either of those things, and we mention them here 
only to indicate that the approach had a higher aspiration 
than merely to move line diagrams about on the screen 
of an oscilloscope. 

Now, at last, we come to the procedure employed in 
analyzing the incoming messages and selecting and di- 
recting the actions taken in response to them. The re- 
sponses were, as suggested earlier, to move things about 
in the room, to make replies by way of the typewriter, and 
— in hope but not in actuality — to add to the internally 
stored knowledge and to the internally stored behavior 
patterns. By knowledge, of course, we mean the contents 
of the several tables mentioned earlier. By behavior pat- 
terns, we mean the set of subroutines available for use in 

The problem of interpreting an incoming message is, 
in the approach we have been describing, to select the 
appropriate subroutine or patterns of subroutines and to 
find the arguments that they require under the prevailing 
circumstances. The selection of subroutines is guided by 
associating subroutines with verbs. The search is carried 
out by a part of the program that examines the internal 
codes that represent the incoming message and a list of 
roles that the message segments may play. Records are 
kept in a matrix during the processing of a message. The 
rows of the matrix are associated with the words of the 
incoming message. The columns of the matrix are associ- 
ated with the possible roles. 

In the version of Ontogeny that was carried to the point 
of demonstration, the processing deals only with words. 



The first step is to look up each word of the incoming 
message in the Property Table and place a tally in each 
cell of the matrix that corresponds to a function that the 
word can fulfill. When this has been done for all the 
words of the message, the task becomes one of finding an 
appropriate and consistent assignment of words to func- 
tions and, at the same time, a correspondence between 
the functions and the argument requirements of a sub- 
routine that goes with the verb. 

The procedure used to carry out this task starts by 
“freezing” the rows and columns of the matrix that con- 
tain only a single tally. The next step is to prepare simpler 
matrix patterns in which each of the words at first associ- 
ated with two or more roles is assigned to a single role. 
These simpler matrixes are then considered one at a time. 

The subroutines corresponding to the word assigned 
to the verb category in the first simplified matrix are ex- 
amined. If one of them has a set of argument require- 
ments that match the roles to which words are assigned, 
then that subroutine is selected, the arguments are sup- 
plied to it, and the response is executed. In an effort to 
get the system into operation quickly, we satisfied our- 
selves with the first subroutine that met the requirements. 
If no subroutine met the requirements of the first assign- 
ment pattern, the second assignment pattern was used, 
and so on. As soon as a suitable subroutine was found, 
supplied with arguments, and executed, the response was 
considered accomplished. The program then simply went 
- into a “listening” mode and waited for the operator to 
take the next step. 

Toward the end of the work on Ontogeny, we were 
planning a set of subroutines that would operate on 
higher-echelon strings than words. With this set of sub- 



routines, there was to be associated a subsystem for keep- 
ing track, in a primitive way, of the “situation.” The sys- 
tem was to be capable of asking, on receipt of a message, 
“Am I already familiar with this message in this context?” 
If so, it was to inquire of itself what response it had previ- 
ously made and how effective the response had been. If 
the result had been sufficiently favorable, then — accord- 
ing to the plan — the system would simply have made 
the same response again and taken notes on its effect. 

In the likely event that no record existed of previous 
experience with the over-all message in the prevailing 
context, then the projected system would work with 
lower-echelon segments of the message, hoping to find 
that one or more of them was already “understood.” In 
the absence of usable prior experience at each echelon, 
the system would drop down to the next-lower echelon 
until it finally came to words. Failing to understand a 
word, or failing to understand a phrase given experience 
with the words of the phrase, it would ask for help. 

Our experience with Ontogeny left us with five main 
impressions: (1) It seems possible, and even likely, that 
we could store up enough substantive information in a 
computer memory to handle the analysis of natural lan- 
guage — semantic as well as syntactic — an analysis ca- 
pable of supporting “reasonable” responses, if only the 
domain of discourse is not very wide. (2) It is probably 
more important to limit the domain of discourse than to 
limit the length or complexity of the input messages. 
(3) Many so-called semantic properties play roles that 
are almost indistinguishable from syntactic roles. The dis- 
tinction between things that are capable of acting with 
initiative as voluntary agents and things that are not, for 
example, seems to be approximately as important as the 



distinction between the active voice and the passive voice 
of verbs. (4) A sympathetic, cooperative, verbal, com- 
munity is a fundamental essential for the development of 
a sophisticated verbal mechanism. To develop complex 
language behavior in a neutral environment would, we 
think, take another long-suffering recapitulation of evolu- 
tion. (5) On the other hand, no one seems likely to de- 
sign or invent a formal system capable of automating 
sophisticated language behavior. The best approach, 
therefore, seems to us to be somewhere between the ex- 
tremes — to call for a formal base plus an overlay of 
experience gained in interaction with the cooperative 
verbal community. 


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BEMER, R. W., Do it by the Numbers—Digital Shorthand. Com- 
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BERKELEY, E. C., and D. G. BOBROW (Eds.), The Programming 
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Report 1055, Bolt Beranek and Newman Inc., Cambridge, 
Mass., August 1963.) 

A Computer-Program System to Facilitate the Study of 
Technical Documents. Report 1103, Bolt Beranek and 
Newman Inc., Cambridge, Mass., November 1963. (To be 
published in American Documentation.) 



BOURNE, C. P., The World’s Technical Journal Literature: An 
Estimate of Volume, Origin, Language, Field, Indexing, 
and Abstracting. Stanford Research Institute, Menlo Park, 
Calif., November 1961. 

CHOMSKY, N., Three Models for the Description of Language. 
IRE Transactions on Information Theory, ¥¥-2 (3), 113- 

124, 1956. 
CHOMSKY, N., Syntactic Structures. Mouton, S’Gravenhage, 

CLAPP, L. C., Associative Chaining as an Information-Retrieval 
Technique. Report 1079, Bolt Beranek and Newman Inc., 
Cambridge, Mass., 1963. 

GIULIANO, V. E., Analogue Networks for Word Association. 
IEEE Transactions on Military Electronics, MIL-7, Nos. 
2, 3, April—July 1963, 221-234. 

GIULIANO, V. E., and P. E. JONES, Linear Associative Information 
Retrieval. In P. Howerton (Ed.), Vistas in Information 
Handling. Spartan Press, Baltimore, Md., 1963. 

ball: An Automatic Question-Answerer. Proceedings of the 
Western Joint Computer Conference, 19, 219-224, 1961. 

GRIGNETTI, M., On the Length of a Class of Serial Files. Report 
1011, Bolt Beranek and Newman Inc., Cambridge, Mass., 
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GRIGNETTI, M., Computer Aids to Literature Searches. Report 
1074, Bolt Beranek and Newman Inc., Cambridge, Mass., 
November 1963). 

GRIGNETTI, M., A Note on the Entropy of Words in Printed Eng- 
lish. Information and Control, 7, 304-306, 1964. (Based on 
Report 1073, Bolt Beranek and Newman Inc., Cambridge, 
Mass., October 1963.) 

Gross, M., On the Equivalence Models of Language Used in 
the Fields of Mechanical Translation and Information Re- 
trieval. Memorandum, Mechanical Translation Group, 
Massachusetts Institute of Technology, Cambridge, Mass., 

HARRIS, Z. S., String Analysis of Sentence Structure. Mouton, The 
Hague, 1962. 



HAYS, D. G., Automatic Language-Data Processing. In H. Borko 
(Ed.), Computer Applications in the Behavioral Sciences. 
Prentice-Hall, New York, 1962, pp. 394-423. 

KLEIN, s., Automatic Decoding of Written English. Ph.D. Thesis, 
University of California, Berkeley, Calif., 1963. 

KLEIN, S., and R. F. SIMMONS, A Computational Approach to 
Grammatical Coding of English Words. Journal of the As- 
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KUNO, S., and A. G. OETTINGER, Syntactic Structure and Am- 
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LICKLIDER, J. C. R., Panel Discussion on Man-Machine Inter- 
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3, 1962. 

LICKLIDER, J. C. R., and W. E. CLARK, On-Line Man-Computer 
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of Information Processing Societies, 21, 113-128, 1962. 

LINDSAY, R. K., Inferential Memory as the Basis of Machines 
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Hill, New York, 1963, pp. 217-233. 

MARILL, T., Libraries and Question-Answering Systems. Report 
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November 1963. 

MCCARTHY, J., Programs with Common Sense. Proceedings of 
Symposium on Mechanization of Thought Processes. Vol. I. 
Her Majesty’s Stationery Office, London, 1959, pp. 77-84. 

M. I. LEVIN, LISP 1.5 Programmer’s Manual. The M.1.T. 
Press, Cambridge, Mass., 1962. 

MOLOSHNAVA, T. N., An Algorithm for Translating from the 
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RHODES, I., A New Approach to the Mechanical Syntactical Anal- 
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ROBINSON, J. J., Preliminary Codes and Rules for Automatic 
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SENDERS, J. W., Information Storage Requirements for the Con- 
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SWANSON, R., Word Correlation and Automatic Indexing. Re- 
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Canoga Park, Calif., 1959. 

SWETS, J. A., Information Retrieval Systems, Science, 141, 245- 
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WALKER, D. E., and J. M. BARTLETT, The Structure of Languages 
for Man and Computer: Problems in Formalization. Paper 
presented at the First Congress of the Information Sciences, 
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WEIZENBAUM, J., Symmetric List Processor. Communications of 
the Association for Computing Machinery, 6, 524-544, 

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Major Features of a Restricted Logistic Grammar for Topic 
Representation. Report 5206-26, Itek Laboratory, Waltham, 
Mass., 1962. 


Abrahams, P. W., 207 
Abstract, 7, 51, 53, 55, 58, 106, 175 
Abstracting, 5 
Access, 16-19 
multiple, 68 
random, 8, 16, 17, 62, 68 
Access time, 16, 18 
Acoustical Society of America, Vili 
of information, 37, 38 
of knowledge, 21-26, 28 
Acquisition policy, 38 
ADAM, 67 
Advisory Committee, xi, xii 
Aims of procognitive systems, 21, 
Airport problem, 190 
Alfred P. Sloan Foundation, vii 
ALGOL, 66, 123, 159 
Algorithm, 55, 81, 91, 113, 114, 180- 
182, 209 
Alphanumeric character, 7, 13-15, 
24, 97, 99, 100, 121-123, 132, 
144, 166, 193-196 


Alphanumeric information, 25, 114, 
constituent, 140 
dependency, 134 
discontinuous-constituent, 138 
hierarchical, 73 
immediate-constituent, 134-136 
of knowledge, 21, 44, 60 
phrase-structure, 134 
predictive, 136 
relational, 82 
semantic, 54, 56, 192, 203 
syntactic, 54, 56, 68, 116, 129, 131- 
133, 140, 141, 192, 203 
transformational, 140 
Analytical semantics, 61 
Andreyey, N. D., 134 
of information, 37 
of knowledge, 24, 26-28, 31, 35, 
675 1025150 
Application system, 27 
APT, 67 



Arthur D. Little, Inc., vii 

Artificial intelligence, 59, 176, 190 

Association, 39, 62, 65, 144, 156, 
174, 180-182, 196, 198 

Associative chaining, 179, 180, 182 

Associative indexing, vii 

Associative memory, 64, 65 

Atlas computer, 169 

Baker, W. O., vi 


Barnes, R. F., 78 

Bartlett, J. M., 140 

Baseball, 140, 153 

Batching, 24 

Behavioral science, 60 

Bell Telephone Laboratories, Inc., 
Vi, Vii 

Bemer, R. W., 146 

Berkeley, E. C., 184 

Berkner, L. V., vi 

Bibliographic citation, 53, 175, 177, 

Bibliography, 51, 52, 56 

Bit, 9, 14, 15, 17, 107, 118, 146, 147, 

158, 194, 197 
Black, F. S., xii, 85, 182-184, 187- 

Bloom, B., xii 

Bobrow, D. G., xii, 131-133, 136, 
139, 140, 177, 184 

Body of knowledge, 1, 5, 6, 9, 15—- 
17, 19-21, 23-25, 29, 32, 33, 
36-38, 43, 44, 61-63, 65-67, 
76, 87, 90, 91, 100, 104, 111, 
112 14 ATT B27 139° 
146, 153, 183, 188 

Bolt, R. H., vi, xii 

Bolt Beranek and Newman Inc., vi, 
Viii, xi, xii, 1, 42 

Book, 2, 4-7, 15, 22, 72, 96, 175, 181 

Boolean algebra, 75, 76 

Boolean function, 175, 176 

Boolean operation, 86 

Borko, H., 206 

- Bourne, C. P., 14 

Bush, V., v, vii, Xii, xiii 
Butterfield, L. H., xii 

Calling a subroutine, 161, 162, 167, 
168, 170, 172, 173 

Canonical form, 54, 82 

Card catalogue, 69, 175, 176 


Card index, 6, 175 
Carnegie Institute of Technology, 42 
Carnegie ; Institution of Washington, 

Catalogue, vi, 7, 143, 152 
card, 69, 175, 176 
Cataloguing, 38 
Cathode-ray oscilloscope, 8, 9, 158, 
166; see also Oscilloscope dis- 
play screen 
Chain of relevance, 180 
Chaining, associative, 179, 180, 182 
Chapman, G. W., xii 
Chapter, 7, 164 
Character, alphanumeric, 7, 13-15, 
24, 97, 99, 100, 121-123, 132, 
144, 166, 193-196 
Character printer, 99 
Chomsky, C., 52 
Chomsky, N., 56, 82, 138, 139 
Churchman, C. W., 102 
Citation, bibliographic, 53, 175, 177, 
Clapp, L. C., xii, 180, 181 
Clapp, V. W., ix, xi 
Clark, W. E., 170 
CLS, 66 
Cluster, 16, 62, 169 
Coated-wire memory, 63 
COBOL, 66 
Cocke, J., 136 
combinational, 145, 146 
compression, 147 
concise, 193, 194, 196, 197 
digital, 39, 46, 52, 65, 73, 96, 99, 
106, 107, 144-147, 161-163, 
178, 193-195 
fixed-length, 144 
hash, 194, 195, 197 
internal, 195, 196, 198, 201 
prime-number, 145 
variable-length, 144 
Cognition, Vii 
Cognitive process, 22 
Cognitive structure, 23, 81 
COGO, 67 
Combinational code, 145, 146 
Command, 9, 53, 114, 118, 122, 124, 
Communication; see also Interaction 
man-computer, 98, 101, 170 

Communication mode, 121-124 
Communication pool, 165-166 
Compiler of programs, 50, 159, 160 
Compiling of programs, 51 
Compression code, 147 
Atlas, 169 
PDP-1, 158, 193, 194 
7090, 184 
Computer-aided learning, 170 
Computer-aided teaching, 127, 170 
Computer-facilitated study, 127, 177 
Computer instruction, 19, 29-31, 49, 
65-107.) 15,1225 160; 161, 
165, 171-174 
Computer memory, 9, 15-18, 25, 26, 
38, 41, 43, 45, 53, 58, 62-64, 
67, 79-81, 102, 104, 106, 121, 
132) 144" 147, 158; 168, 171, 
173, 184, 193, 194, 200, 203 
Computer processor, 6, 9, 15, 19, 38, 
42, 58, 65, 163, 171; see also 
Processing of information 
Computer program, 8, 29, 35, 39, 
50, 57, 58, 66, 84, 85, 92, 94, 
104, 107-112, 115, 121, 123, 
125. 129" 132) 134. 136, 137, 
140, 158, 160, 163, 165, 166, 
168, 170-177, 180, 181, 185— 
187, 192, 193, 195, 197, 199 
Computer-program model, 111 
Computer programmer, 104, 105, 
107-110, 119, 121, 162-166 
Computer programming, 48-51, 58, 
65, 104, 125, 158, 160, 166, 
176, 182, 191, 197 
heuristic, 190 
Computer science, 60 
Computer system 
multiple-access, 68 
multiple-console, 42 
Concise code, 193, 194, 196, 197 
Concordance, 181 
Console, 33, 41, 46-48, 52, 69, 92- 
94, 96, 100-103, 105, 113, 
Constituent analysis, 140 
Content-addressable memory, 8, 64, 
Content word, 132 
Control of computers, 41, 47-49, 51, 
37; 66, 67, 92, 97, 98, 101, 
1035/0113; 120, 122, 124 


Control language, 114, 122 
Control mode, 121-124 
Control station, 33 
Conversation between man and 
computer, 36, 124, 125 
Coordinate index, 74, 75, 143, 181 
Core memory; see Memory, mag- 
Corpus, 9, 14-16, 20, 24, 25, 28, 38, 
41, 44, 61-63, 67, 68, 87, 
125, 153, 154, 179-181, 183-— 
188, 191 
Correlation, 55, 77, 78, 112, 117, 118 
Council on Library Resources, Inc., 
Vie Vil, 1x. x1, 12 2) 100 
of pertinence, 148, 149 
for procognitive systems, 21, 32- 
for the program of study, 2 
Cryogenic memory, 16, 63 

Data base, 26, 31, 53, 66, 88, 89, 

Data structure, 112, 119 

IDID AE, 1 1? 

Debugging system, 121 

DECAL, 66, 159, 160, 162, 174, 175 

Decision theory, 150 

Decoding, 143, 146, 147, 200 

Defense, Department of, viii 

Defense Documentation Center, 74 

Delay-line memory, 16 

Delimiter, 181 

Dependency analysis, 134 

Dependency grammar, 132 

Descriptor, 7, 45, 48, 50, 54, 55, 64, 
69, 70, 74, 142 

Descriptor structure, 54, 55 

Desk, 6, 9, 33 

Dewey Decimal Classification Sys- 
tem, 61 

Dictionary, 68, 132 

Digital code, 39, 46, 52, 65, 73, 96, 
99, 106, 107, 144-147, 161- 
163, 178, 193-195 

Digital Equipment Corporation, 158 

Digital memory, 17, 18, 142-144 

Direct file; see File 


Discontinuous-constituent grammar, 




Display, (45 15,0756 29 oS Sai 
47, 50, 56, 58, 66, 92, 94— 
97, 100-103, 105-108, 115, 
118, 120, 121, 158, 159, 166, 
168, 170, 172, 173, 176, 183 

Display screen; see Oscilloscope dis- 
play screen 

Dissemination of information, 11 

Distributed system, 37 

Document, 2, 6, 13, 14, 24, 29, 30, 
Ko, shh, SS, Ss), Oil, 70), 745 7A 
107, 111-114, 142-144, 148, 
IBYA, W535 US, GS, Al, ie 

Document retrieval, 45, 46, 76, 78, 
106, 129, 153, 177; see also 

Document room, 188 

Documentation center, 7, 42, 74, 175 

Drum memory; see Memory, mag- 

Dynamic model, 112 

Dynamic storage, 169 

Editing, 32, 97, 113 
post-, 137 
program, 106, 107 
Edwards, D. J., 207 
Elkind, J. I., xii 
Encoding, 143, 144, 146, 147, 200 
English language; see Language, 
Entropy; see Informational meas- 
ure; Redundancy 
Erasable display, 94 
Erase mode, 47 
Exec, 160-162, 164, 166-170, 174 
Executive program, 158-160 

FACT, 66 

Fact, computer handling of, 36, 39, 
70, 72, 77, 124, 183, 188, 200 

Fact retrieval, 129 

Feigenbaum, E., 207 

Feldman, J., 207 

Field of knowledge, 9, 15, 18, 19, 
sl cul, CBE CMe Si) Bi 2, Sh 
67, 150 

Field—oriented language, 31, 36, 66, 
67, 119, 120 

File, 75, 110, 129, 142, 143, 145, 164, 
165, 174, 175, 180, 181 

direct, 174 


hierarchical, 117 
inverse, 174 
Film memory 
magnetic, 16, 17, 63 
photographic, 18, 63 
Film projector, 47, 96 
Fixed-length code, 144 
Flow of information, 26, 28 
Ford Foundation, vi 
Formal language, 39, 71, 104, 112, 
183, 188 
Formalism, 71, 85, 112, 153 
Formalization, 61, 68, 112, 125, 154, 
187, 189 
Format, 3, 30, 49, 52, 57, 92, 109, 
113, 115, 119, 124, 126, 165- 
167, 174, 185, 199 
FORTRAN, 66, 67 
FRAP, 160 
Function word, 132, 175, 180 
Fund of knowledge, 21, 22, 24, 26— 
28, 32, 34, 35, 38, 39, 41, 43- 
45, 58, 60, 61, 65, 192 

Geometric space, 71 
Giuliano, V. E., 56, 78 
GPSS, 67 
Graduate Center of the Southwest, 
Graph, 7, 24, 29, 171, 172, 177, 178 
Graph-control language, 106, 111- 
iets). ie 
Graph theory, 180, 181 
Grammar, 71, 82, 131, 132, 134 
dependency, 132 
discontinuous-constituent, 137 
phrase-structure, 132 
predictive, 132 
string-transformational, 132, 140 
transformational, 117, 132 
Grammatical category, 82, 117, 132, 
134, 137, 139, 140 
Grammatical class, 134 
Grammatical structure, 132 
Green, B. F., 52, 140, 153 

Grignetti, M., xii, 142, 144-147, 

Gross, M., 134 

Group-computer interaction, 100, 

102; see also Communication 
Growth of body of recorded knowl- 
edge, 15, 20, 37, 127 

Hardware, 58-60, 63, 65, 66, 111, 
118, 159 

Harris, Z. S., 140 

Harte te. 207 

Harvard University, viii, 136 

Hash code, 194, 195, 197 

Haskins, C. P., vii, xii 

Hays, D. G., 134 

Heilprin, L. B., xi 

Heuristic, 39, 68, 91 

Heuristic programming, 190 

Hierarchical analysis, 73 

Hierarchical file, 117 

Hierarchical index, 73 

Hierarchical structure, 9, 40, 80, 133 

Hierarchy, 7, 29, 42, 72, 74-76, 80, 
81, 165, 169 

memory, 63, 79, 80, 81, 169 

High-level (high-order) language, 
86-88, 106, 111, 112, 122 

Howerton, P., 206 

Idea; see Fact, computer handling 
Immediate-constituent analysis, 134— 
Immediate-constituent grammar, 137 
Index, 7, 142, 143, 152 
card, 6, 175 
coordinate, 74, 75, 143, 181 
hierarchical, 73 
permuted-title, 175 
Indexing, 5, 38 
associative, vii 
Inference, 24 
Information; see also Processing of 
information, Representation 
of information 
acquisition of, 37, 38 
alphanumeric, 25, 114, 166 
application of, 37 
dissemination of, 11 
flow of, 26, 28 
meta-, 36, 112 
organization of, v, 1, 4, 11, 25, 
32, 38, 46, 53, 61, 68, 70, 71, 
recorded, 61 
retrieval of, vi, 1, 4, 5, 8, 11, 60, 
64, 69-71, 73, 76, 77, 94, 106, 
125, 129, 148-152, 179-181 
science of, 31, 113 
storage of, vi, 1, 4, 5, 7, 8, 11, 29, 


35, 42, 60, 70, 71, 73, 76, 125, 
143, 144, 146, 162, 173, 183, 
transformable, 2, 6 
Information base, 85 
Information-processing utility, 33 
Information structure, 44, 65, 111, 
113-119, 125, 169 
Informational measure, 11, 13, 142, 
146, 147; see also Entropy; 
Input language, 89, 160 
Instruction, computer, 19, 29-31, 
AD 65a Of i522. 1160; 
165, 171-174 
Insight, 32, 58, 109 
Intelligence, 29, 58, 187, 191 
artificial, 59, 176, 190 
group-computer, 100, 102 
man-computer, 11, 50, 51, 66, 90- 
92, 98, 100, 102, 104, 121, 
123. 1265 1591167 
man-machine, 123, 159, 160 
on-line, 51, 124, 125 
symbiotic, 123 
Interaction language, 104, 123-125 
Interface, 37, 41; see also Inter- 
man-machine, 91, 92 
man-computer, 92 
physical, 92, 93, 102-105, 116 
Internal code, 195, 196, 198, 201 
International Business Machines 
Corporation, vii, 18, 85, 136, 
Interpreter program, 159, 165, 166 
Interpreting computer programs, 160 
Introspection, 167, 168, 170-172 
Inverse file, 74 
IPL, 66, 162 
Itek Corporation, vii 

Jones, P. E., 78 
Journal, 4, 7, 36, 72, 89 

Kain, R. Y., xii, 177 
Keeney, B. C., xii 
Kelly, H., 134 
Kernel form, 29 
Key word, 175 



Keyboard, 46, 48, 97-100, 103 
King, G. W., vii, xii 
Klein, S., 52, 134, 136 
KLS, 66 
Knowledge; see also Body of knowl- 
edge; Representation of 
analysis of, 21, 44, 60 
application of, 24, 26-28, 31, 35, 
67, 112, 150 
field of, 9; 15; 1:8, 19; 315 415 43; 
AVE) S85 Shs SG G¥/5 EW) 
fund of, 21, 22, 24, 26-28, 32, 34, 
35, 38, 39, 41, 43-45, 58, 60, 
61, 65, 192 
organization of, 5, 6, 15-17, 20- 
22, 24-26, 28, 36, 38, 43, 44, 
62, 63, 65, 68, 76, 82, 87, 111, 
iG ale) 
recorded, 1, 2, 6, 11, 60 
store of, 11, 28, 37, 40, 68 
USO As (5 Ail AS, SP Sy7/5 OS OL) 
Kuipers, J. W., 78 

Laboratory, 22, 93 
Land, E. H., vii 
control, 114, 122 
English, 48, 50-52, 67, 87-89, 131, 
135, 142, 146, 153, 190 
field-oriented, 31, 36, 66, 67, 119, 
formal, 39, 71, 104, 112, 183, 188 
graph-control, 106, 111-113, 115, 
high-level (high-order), 
106; Ade 2122: 
input, 89, 160 
interaction, 104, 123-125 
low-level (low-order), 86 
machine-independent, 39 
natural, 46, 48-51, 67, 68, 78, 85, 
87, 89, 99, 104, 111-113, 120, 
12S ples ileal (3292208 
on-line, 122, 124, 125, 160 
organizing, 111, 112, 118, 119, 122 
problem-oriented, 9, 119, 120 
procedure-oriented, 9, 29, 30, 31, 
36, 66, 67 
programming, 30, 104, 105, 110, 
111, 119, 122, 124, 159, 160 
publication, 37 



query, 117 
representation, 111-113, 117, 118, 
specialized, 66, 105, 116 
user-oriented, 66, 119, 127, 159, 160 
Language system, 125 
Laughery, K., 52 
Learning, computer-aided, 170 
Levin, M. I., 207 
Leviticus, v 
Librarian, 37, 127 
Library, v, vi, vii, viii, 1, 2, 4-7, 13, 
14, 22-24, 33, 42, 69, 102, 129, 
131, 1S Sae142 4B 52 lose 
1575 174, 175; 188 
Library of Congress, 14 
Library science, 3, 60 
Licklider, J. C. R., viii, xii, 14, 170, 
Light pen, 8, 9, 37, 101, 103, 120, 
158, 178 
Limit, physical, 17, 20 
Lincoln Laboratory, 140 
Lindsay, R. K., 137, 140 
Line printer, 103 
Linguistics, 60, 71, 82, 86, 92, 104, 
LISP, 66, 162, 184, 185, 207 
List. 295305 Son 2 lil aoe aoe 
175, 196 
pushdown, 162, 163 
List processor, 65 
List structure, 9, 65, 117 
Little, Arthur D., Inc., vii 
Logic, 78, 113, 183, 184 
mathematical, 60 
symbolic, 154, 182 
Logical category, 54 
Logical connective, 72, 74 
Logical deduction, 191 
Logical operator, 155 
Logical relation, 55 
Low-level (low-order) language, 86 
LUCID, 67 

McCarthy, J., xii, 85, 184, 188, 190, 

Machine-independent language, 39 

Machine translation, 50, 52, 56, 68 

MACRO, 159 

MAD, 66 


Magnetic-core memory, 16, 17 

Magnetic-disk memory, 16, 18, 63 

Magnetic-drum memory, 16 

Magnetic-film memory, 63 

Magnetic-tape memory, 16, 18, 158 

Man-computer communication, 98, 
101, 170 

Man-computer interaction, 11, 50, 
51, 66, 90-92, 98, 100, 102, 
104, 121, 123, 126, 159, 167 

Man-computer intermedium, 92 

Man-machine communication; see 
Communication; Interaction 

Man-machine interface, 91, 92 

Marill, T. M., xii, 84, 152-156, 179, 

Marking unit, typewriter, 46 

Massachusetts Institute of Technol- 
ogy, Vii, viii, xii, 42, 95, 106, 

Match, 32, 55, 64, 109, 110, 120, 
185, 189 

Matching structure, 55 

Mathematical logic, 60 

Mathematical model, 111 

Matrix, 112, 114, 115, 117, 201, 202 

Meaning, 54, 154 

Mechanical translation, 50, 52, 56, 

Memekx, Vii, xii 
Memory; see also Computer mem- 

associative, 64, 65 
coated-wire, 63 
content-addressable, 64, 65 
cryogenic, 16, 63 
delay-line, 16 
digital, 17, 18, 142-144 
magnetic-core, 16, 17 
magnetic-disk, 16, 18, 63 
magnetic-drum, 16 
magnetic-film, 16, 17, 63 
magnetic-tape, 16, 18, 158 
photographic-film, 18, 63 
photographic-plate, 63 
processible, 16, 25, 62, 146 
random-access, 8, 16-19, 62, 63 
relational, 65 
serial-access, 17-19 
thermoplastic, 64 
thin-film, 16, 17, 63 

Memory Course, 171-173 

Memory hierarchy, 63, 79, 80, 81, 



Memory organization, 25, 63, 65 
Metainformation, 36, 112 
Metric space analogy, 77 
Microphone, 47, 49 
Minsky, M., xii, 106 
Mitre Corporation, 140 
Mode, 36, 48, 49, 51, 52, 100, 107, 
communication, 121-124 
control, 121-124 
erase, 47 
Model, 42, 44, 71, 78, 117, 199 
computer-program, 111 
dynamic, 112 
mathematical, 111 
Module, 17 
Moloshnava, T. N., 134 
Monitoring, 6, 26, 28, 59, 105, 123 
Morris, J. C., xii 
Morse, P. M.., vii, xii 
Multiple-access computer system, 68 
Multiple-console computer system, 

National Bureau of Standards, 136 

National Science Foundation, vii 

Natural language; see Language, 
natural; Language, English 


Neuronal element, 24 

Oettinger, A. G., 56, 57, 136, 137 
On’ line5)353555;) 663,985) 121.) 123; 
1255, 158-0 1595170 
On-line interaction, 51, 124, 125, 160 
On-line language, 122, 124, 125 
On-line programming of computers, 
Ontogeny, 187, 192, 197, 201, 202 
Operation, computer, 19, 30, 32, 64, 
88, 98, 104, 111, 113-116, 
118, 119, 161, 164, 188, 197, 
Operator, syntactic, 80 
of information, v, 1, 4, 11, 25, 32, 
38, 46, 53, 61, 68, 70, 71 153 
of knowledge, 5, 6, 15-17, 20-21, 
2426, 28, 36, 38, 43, 44, 62, 
635, 65; 68, 76:782,087,, 111; 
117, 139 
memory, 25, 63, 65 



Organizing language, 111, 112, 118, 
119, 122 

Oscilloscope, cathode-ray, 8, 9, 158, 

Oscilloscope display screen, 31, 36, 
46-49, 57, 92, 93, 95, 96, 101, 
103, 106, 107, 109, 115, 120, 
159, 166, 173, 176-178, 200, 

Osgood, C. E., 77 

Overcentralization, 37 

Overhage, C., xii 

Page, 4-7, 15, 29, 37, 47, 93, 177 

Paragraph, 7, 29, 30, 70, 87, 164, 
179, 180, 181, 197 

Park, D., xii 

Parsing, 56, 140 

Passage retrieval, 76 

PDP-1 computer, 158, 193, 194 

Peek-A-Boo Card, 74, 75 

Pen, light, 8, 47, 49, 93 

Pennsylvania, University of, 140 

Perceptron, 42, 176 

Permuted-title index, 175 

Pharmaceutical research, 24 

Photographic-film memory; see 

Photographic-plate memory; see 

Phrase, 50, 54, 133, 135, 136, 193-— 
197, 203 

Phrase structure, 138 

Phrase-structure analysis, 134 

Phrase-structure grammar, 132 

Physical intermedium, 92, 93, 102- 
105, 116 

Physical limit, 17, 20 

Pierce, J. R., vii, xii 

Piore, E. R., vii, 18 

Plan for procognitive system, 7, 11, 
20, 39, 40 

Pneumatic-tube station, 52 

Polaroid Company, vii 

_ Postediting, 137 

Predicate calculus, 53, 54, 71, 84, 
85, 89, 153, 156 

Predictive analysis, 136 

Predictive grammar, 132 

Prescription, retrieval, 28, 29, 54, 55, 
71-74, 168, 175-178, 181 

Prime-number code, 145 


Princeton paradigm of computer or- 
ganization, 42 
character, 99 
line, 103 
Problem-oriented language, 9, 119 
Procedure, computer, 8, 29, 39, 45, 
56, 58, 92, 123, 157, 184, 195, 
197, 199, 201, 202 
Procedure-oriented language, 9, 29- 
31, 36, 66, 67 
Processible memory, 16, 25, 62, 146 
Processing of information, viii, 19, 
20, 28, 29, 41, 42, 44, 45, 
48-50, 64, 66, 67, 86, 88, 116, 
117, 124 125512913147 
156, 161, 167, 183, 184, 185, 
187, 192, 194, 195, 201 
Processor, 6, 9, 15, 19, 38, 42, 58, 65, 
163 Sel 
list, 65 
Procognitive function, 129 
Procognitive system, 6-8, 11, 13, 21, 
26-29, 31-36, 40-42, 45, 46, 
48, 57, 59, 60, 63, 64, 67-70, 
79, 86, 90, 97, 98, 100, 101, 
Ne OSs, Till, at, aul ails), 
119120 12385 12512 7eelsie 
138, 143, 146, 153, 157, 158- 
160, 169, 192 
aim of, 11, 21, 31 
criterion for, 11, 21, 32-39 
defined, 6—8 
plan for, 7, 11, 20, 39, 40 
requirement for, 11, 21 
Program; see also Computer pro- 
compiling of, 51 
editing, 106, 107 
executive, 158-160 
system, 105, 110 
Program compiler, 50, 159, 160 
Program Graph, 171, 172 
Program structure, 173 
computer, 104, 105, 107-110, 119, 
121, 162-166 
system 105, 116, 118, 123 
Programming of computers, 48-51, 
583) 655) 1045125. aS Sa G0; 
166, 176, 182, 191, 197 
heuristic, 190 
on-line, 159 

Programming language, 30, 104, 105, 
OMT 1192 122 124. 159, 

Programming system, 105-107, 109- 
111, 162 

Projector, film, 47, 96 

Publication lag, 37 

Publication language, 37 

Pushdown list, 162, 163 

QAS-5, 188-190 

Quantificational schema, 153-155 

Query, 50, 53 

Query language, 117 

Question-answering system, 46, 52, 
535555070; 85, 107, 111, 129, 
152, 182-184, 188, 190, 191 

RAND Corporation, 42, 96, 134, 
RAND Tablet, 95, 96 
Random-access memory, 8, 16-19, 
62, 63 
Raphael, B., xii, 84, 177 
Recorded information, 61 
Recorded knowledge, 1, 2, 6, 11, 60 
Recovery of documents, 38 
Recursion, 9, 26, 84, 127, 162, 169 
Redundancy, 13, 14, 142 
logical, 55 
semantic, 49, 51, 54, 61, 68, 125 
syntactic, 133 
Relational analysis, 82 
Relational memory, 65 
Relational network, 65, 82-84, 89, 

117, 154, 182 
Relevance, 55, 56, 63, 71, 182, 183, 

Relevance network, 60-63 
Relevance space, 62, 63 
Representation of information, 76, 
Soothes, 1182 125, 134, 142, 
144, 153, 155, 156, 193-195 
Representation of knowledge, 27, 28, 
44, 65, 68, 76, 78, 82, 86, 104, 
118, 126 
Representation language, 
D7, 118, 122, 125 
Retention policy, 38 
of documents, 45, 46, 76, 78, 106, 
129) 153; 177 



of facts, 129 
of information, vi, 1, 4, 5, 8, 11, 
60, 64, 69-71, 73, 76, 77, 94, 
106, 125, 129, 148-152, 179- 
of passages, 76 
Retrieval prescription, 28, 29, 54, 55, 
71-74, 168, 175-178, 181 
Returning control to calling routine, 
161, 162, 167, 168, 172, 173 
Rhodes, I., 56, 136 
Robinson, J. J., 136 
Role indicator, 75, 76 
Routine, computer, 57, 
115, 162, 164, 178 
Ruggles, M. J., xi 
Ruly English, 88 

106, 114, 

SAGE System, 17 
Schema, 3, 4, 6-9, 11, 24, 25, 27, 35, 
42, 43, 59, 64, 65, 71, 78, 84, 
93, 97-100, 143, 148, 152, 154, 
180, 182 
quantificational, 153-155 
Screen, oscilloscope display, 31, 36, 
46-49, 57, 92, 93, 95, 96, 101, 
103, 106, 107, 109, 115, 120, 
1595 166, 4973; .976, 1:77; 178; 
200, 201 
Search, 26, 29, 32, 169, 176, 178, 
179, 195, 199, 200 
Section, 164 
Self-organization, 91, 126, 127 
Semantic analysis, 54, 56, 192, 203 
Semantic differential, 77 
Semantic factor, 141 
Semantic link, 60 
Semantic net, 50, 51, 117, 152, 154— 
156, 182 
Semantic property, 198 
Semantic relation, 49, 51, 61, 68, 125 
Semantics, 51, 68, 86, 137, 192 
analytical, 61 
Senders, J. W., xii, 14, 15 
Sentence, 2, 7, 24, 29, 30, 54, 56, 57, 
60; 70=72. JOS 812482, 113; 
132-138, 140, 154-156, 163, 
164, 179, 180, 182, 186, 194, 
Serial-access memory, 17-19 
Set, 71, 72, 74, 76, 86, 154, 179 
Shannon, C. E., 146, 147 
Simmons, R. F., 52, 134 

PA | 


Simulation, 10, 31, 65, 66, 175, 176 
Sketchpad, 67, 94 
SLIP, 66, 67 
Sloan, Alfred P., Foundation, vii 
Social science, 60 
Software, 58-60, 66, 69 
SOL, 67 
geometric, 71 
work, 93, 102 
Space analogy 
metric, 77, 78, 117 
topological, 77 
Specialized language, 66, 105, 116 
Specific-Question-Answering System, 
184, 188, 189 
Spoken output, 47 
Stack, 162 
Stanford University, 85 
Stevenson, E. P., vii 
Storage of information, vi, 1, 4, 5, 
erSs Milee29. 395142. 1605 0% 
Tl, 13; 16, 125, 143.144" 146. 
162, 173, 183, 196 
dynamic, 169 
temporary, 162 
Store, 5, 15, 30, 45, 46, 53, 101, 111, 
142; see also Knowledge, 
store of 
String, 24, 48, 115, 122-124, 132, 
134, 140, 142, 146, 155, 164— 
166, 175, 181, 184, 193-196, 
198, 202 
String-transformational grammar, 
132, 140 
Study, computer-facilitated, 127, 177 
Subfield of knowledge, 15, 18, 19, 
28) 415 435) 4457465" 505 

Subprogram, computer, 121, 161, 
174, 175 

Subroutine, computer, 8, 39, 160- 
165, 167-170, 173, 199-202 

Subset, 71-74, 76, 126, 148, 178, 179 

Sire, (Ge Ue, Wil 

Swanson, R., 77 

Swets, J. A., xii, 149, 150 

Symbiont, xii, 177, 179 


Symbiotic interaction, 123 

Symbolic logic, 154, 182 

Synonym, 30, 54 

Syntactic analysis, 54, 56, 68, 116, 
129, 131-133, 140, 141, 192, 

Syntactic category, 54, 71, 125, 134, 
140, 141 

Syntactic class, 119 

Syntactic factor, 141 

Syntactic form, 30 

Syntactic pattern, 136, 137, 140 

Syntactic property, 198 

Syntactic relation, 133 

Syntactic structure, 60, 75, 82, 123, 
131, 139 

Syntax, 51, 56, 58, 86, 105, 124, 192 

System Development Corporation, 
42, 134, 136 

System program, 105, 110 

System programmer, 105, 116, 118, 

System science, 60 

System specialist, 38, 111-119 

Table29- 5 7-112: 
168, 169, 195 

Tannenbaum, P., 77 

Tape, magnetic; see Memory, mag- 

Teaching, computer-aided, 127, 170 

Teaching machine, 36 

Teager, H. M., 95 

Teager Table, 95 

Telecommunication, 33, 41, 44 

Telecomputation, 33 

Telephone, 22, 101, 110 

Teletypewriter, 98 

Television, 101 

Texas, University of, 137 

Text 7, 9, 19, 29, 53-55, 61, 67, 68, 
88, 113-115, 122, 129, 131, 
132, 137, 142, 146, 147, 158, 
164, 166, 175, 177-179, 181, 

computer analysis of, 56 

Thermoplastic memory, 64 

Thesaurus, 7, 29, 30, 50, 54, 55, 64, 
70, 73 

Thin-film memory; see Memory 

Thompson, F. B., 141 

Time sharing, 9, 42 

Title, 175 

114, 163-165, 

Topological space analogy, 77 
Transform, 29, 30, 32, 47, 185, 189 
Transformable information, 2, 6 
Transformation, 27, 82, 89, 138, 139, 
Transformational analysis, 140 
Transformational grammar, 82, 117, 
132, 134, 137, 139, 140 
Translation, 13, 25, 68, 85, 89, 111- 
113, 124, 126, 160, 169 
machine, 50, 56, 68 
mechanical, 52 
Tree, 40, 57, 117, 134 
Tree diagram, 57, 140 
“Trie” structure, 117 
Typewriter, 46-50, 92, 97-100, 103, 
1585 165; 166; 168, 175, 177, 
178, 193, 196, 197, 201 

Uniterm, 7 

Use of knowledge, 2, 6, 21, 26, 32, 
37, 60, 90 

User-oriented language, 66, 119, 127, 
159, 160 

User station, 9, 33, 40-42, 44, 45, 99, 

Utility, information-processing, 33 

Variable-length code, 144 
Volume, 7 

Walker, D. E., 140 
Weaver, W., vii 
Weizenbaum, J., 67 
Wiener, N., 146 
Williams, T. M., 78 
Wolf, A. K., 52 
Word) 25075 295 30847 51; 52, 54; 
GSarcle Seo 4.135, 
146, 147, 163-165, 175, 181, 
184, 193-197, 201, 202 
computer, 163 
content, 132 
function, 132 
key, 175 
Work space, 93, 102 

Yngve, V. H., 56 

Zipf, G. K., 146 


025.078 lLicklider, J.C.R. 

L698 Libraries’ of the